From Knowledge to Intention: Developing and Validating a Scale for K-12 Teachers' Readiness to Teach AI

From Knowledge to Intention: Developing and Validating a Scale for K-12 Teachers' Readiness to Teach AI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Knowledge to Intention: Developing and Validating a Scale for K-12 Teachers' Readiness to Teach AI Hasan Tokatlı, M. Fatih Erkoç This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9291938/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The growing integration of artificial intelligence (AI) into society has increased demand for K-12 AI education, placing information technology (IT) teachers at the forefront of curriculum delivery. However, validated instruments assessing IT teachers' readiness and motivation toward teaching AI remain scarce. This study developed and validated the Readiness to Teach AI and Behavioral Intention Scale (RTAI-BIS), grounded in the Technological Pedagogical Content Knowledge (TPACK) framework and the Theory of Planned Behavior (TPB). An initial pool of 294 items was refined through literature review, expert panel evaluation (n = 6), and pilot testing, yielding a 45-item candidate scale. Exploratory Factor Analysis (EFA) with 392 IT teachers in Turkish schools yielded a five-factor structure explaining 73.79% of variance. Confirmatory Factor Analysis (CFA) with an independent sample (n = 258) validated a refined four-factor, 26-item model with acceptable fit (Comparative Fit Index [CFI] = .943, root mean square error of approximation [RMSEA] = .079, standardized root mean square residual [SRMR] = .061). The final four factors are Technological, Pedagogical, and Content Knowledge for Teaching AI (TPACK-TAI), Attitude Toward AI (ATA), Technological Knowledge for Teaching AI (TK-TAI), and Disposition Toward Teaching AI (DTAI), with two knowledge factors capturing related but distinguishable aspects of instructional readiness. Reliability analyses demonstrated strong internal consistency (Cronbach's α = .921–.974). The RTAI-BIS offers a psychometrically sound tool for assessing IT teachers' readiness to teach AI and their behavioral intentions, with applications in teacher training and curriculum implementation. Educational Psychology Artificial Intelligence and Machine Learning readiness to teach AI behavioral intention scale development TPACK Theory of Planned Behavior K-12 education Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Artificial intelligence (AI) is reshaping work, knowledge production, and the skill expectations placed on education systems (Di Battista et al., 2023 ; Frey & Osborne, 2017 ). Schools feel this shift acutely. K-12 systems face growing pressure to prepare students to understand AI's logic, applications, and social implications (Bessen, 2018 ; Martinez, 2018 ; Panth & Maclean, 2020 ). 1.1 AI Education at the K-12 Level Since 2019, K-12 AI education has shifted from a peripheral topic to an emerging curriculum field, supported by frameworks that specify what students should know about AI and how those ideas can be taught in age-appropriate ways (Kim et al., 2021b ; Long & Magerko, 2020 ; Touretzky et al., 2019 ). Reviews of this literature converge on three themes. First, K-12 AI education is expected to address foundational concepts, machine learning, and the social consequences of AI rather than narrow technical training alone (Casal-Otero et al., 2023 ; Rizvi et al., 2023 ; Su et al., 2024 ). Second, instruction tends to rely on applied, student-centered approaches such as project-based learning and collaborative design tasks (Ng et al., 2023 ; Sanusi et al., 2023 ; Su et al., 2022 ). Third, and most critically for implementation, teacher preparedness remains a persistent bottleneck: teachers need domain knowledge, pedagogical strategies, and confidence to teach AI, yet these capacities are unevenly developed and rarely assessed through robust instruments (Kim et al., 2021a ; Ng et al., 2023 ; Yue et al., 2024 ). These global patterns are evident in Turkey, where the broader policy movement identified by UNESCO ( 2021 ) as dependent on both curriculum design and teacher capacity is already underway. The National Artificial Intelligence Strategy 2021–2025 (T.C. Cumhurbaşkanlığı Dijital Dönüşüm Ofisi [CBDDO], 2021) and the AI Applications curriculum issued by the Ministry of National Education (T.C. Millî Eğitim Bakanlığı [MEB], 2023) indicate that AI teaching is no longer a speculative future concern, but an implementation challenge for schools and teachers. 1.2 The Critical Role of Teachers Teachers are central to whether AI curricula become meaningful classroom practice. In many school systems, and particularly in the Turkish context, information technology (IT) teachers are among the most likely implementers of formal AI instruction. Their preparedness therefore has both a competence dimension, concerning whether they believe they have the knowledge and pedagogical capacity to teach AI, and an intention dimension, concerning whether they are willing and motivated to do so. Existing studies point to this dual challenge, but they do so unevenly. Research has examined teachers' motivation and behavioral intention toward AI teaching (Chai et al., 2024 ; Ayanwale et al., 2022 ; Sanusi et al., 2024 ), their AI-related knowledge or TPACK readiness (Kim et al., 2021a ; Yue et al., 2024 ), and instruments for adjacent constructs such as using AI as a pedagogical tool, general AI self-efficacy, or teacher acceptance of AI (Celik, 2023 ; Guo et al., 2025 ; Ramazanoğlu & Akın, 2025 ; Wang & Chuang, 2024 ). However, these strands remain largely separate, and none addresses readiness to teach AI as curriculum content with an integrated measurement approach. Cross-national evidence reinforces this gap: Du et al. ( 2023 ) found that even teachers with moderate AI literacy felt unprepared to adapt AI curricula to their classrooms, and Addo ( 2023 ) reported similar barriers among UK teachers — highlighting the disconnect between general awareness and teaching-specific readiness. To our knowledge, no validated instrument jointly measures knowledge-based readiness and behavioral intention for teaching AI as a school subject — a gap especially acute in Turkey, where policy attention to AI education has advanced more quickly than validated assessment tools. 1.3 Purpose of the Study Accordingly, this study aimed to develop and validate a psychometrically sound instrument, the Readiness to Teach AI and Behavioral Intention Scale (RTAI-BIS), for measuring IT teachers' readiness to teach AI and their behavioral intentions toward doing so. The study was guided by the following research questions: 1. What is the factor structure of the RTAI-BIS as determined by exploratory factor analysis? 2. To what extent does confirmatory factor analysis support the factor structure identified through EFA? 3. Does the RTAI-BIS demonstrate acceptable levels of reliability? The RTAI-BIS is designed to support readiness profiling of teacher populations, evaluation of professional development interventions, and cross-regional comparison of teacher preparedness for AI instruction. 2. Theoretical Framework The RTAI-BIS was informed by two complementary frameworks: the Technological Pedagogical Content Knowledge (TPACK) framework and the Theory of Planned Behavior (TPB). Together, they provide a basis for conceptualizing teacher preparedness as both a competence issue and an intention issue. 2.1 Technological Pedagogical Content Knowledge (TPACK) The TPACK framework (see Fig. 1 ) extends pedagogical content knowledge by emphasizing that effective teaching with technology requires the coordinated use of content knowledge, pedagogical knowledge, and technological knowledge (Koehler & Mishra, 2009 ; Mishra & Koehler, 2006 ; Shulman, 1986 , 1987 ). In the context of AI education, this means that teachers need not only to understand AI concepts, but also to select suitable pedagogical approaches and use technological tools in ways that make those concepts teachable to K-12 learners. TPACK has become a common lens for examining teacher readiness in technology-rich settings (Koehler & Mishra, 2009 ; Kim et al., 2021a ). In AI education specifically, Kim et al. ( 2021b ) argued that TPACK can frame the knowledge teachers need for K-12 AI instruction, and Yue et al. ( 2024 ) showed that teachers' AI-related content and technological knowledge may lag behind their general pedagogical confidence. At the same time, newer AI-focused extensions such as Intelligent-TPACK and AI-TPACK often target teachers' capacity to use AI as a teaching tool rather than to teach AI as curricular content (Celik, 2023 ). The RTAI-BIS adopts TPACK in this latter, subject-teaching sense: the readiness component of the RTAI-BIS was designed to capture teachers' perceived readiness to teach AI concepts, practices, and applications. Within the TPACK model, technological knowledge (TK) is one component of the broader TPACK framework rather than a stand-alone representation of integrated instructional knowledge (Mishra & Koehler, 2006 ). However, in emerging subject areas such as AI education — where the tools themselves are the curricular content — TK-related competencies may be empirically separable from the broader pedagogical-content integration that TPACK represents. Whether this separation manifests in the present context is treated as an empirical question. 2.2 Theory of Planned Behavior (TPB) The TPB explains behavior through the mediating role of intention, which is shaped by attitude toward the behavior, subjective norms, and perceived behavioral control (Ajzen, 1991 , 2020 ). Applied to AI teaching, these components concern whether teachers see teaching AI as worthwhile, perceive social support or pressure around it, and believe they have sufficient capability and resources to do it. This framework is relevant because intention-based models consistently explain teachers' adoption of new technologies and practices (Ajzen, 2011 , 2020 ; Davis, 1989 ; Scherer et al., 2019 ; Venkatesh et al., 2003 ). TPB has been applied to domain-specific teaching intentions — for example, Lin and Williams ( 2016 ) modeled preservice teachers' STEM teaching intentions and found perceived behavioral control and subjective norms to be the strongest predictors. In AI education specifically, prior work has linked intention to attitudes, efficacy beliefs, confidence, and perceived relevance (Ayanwale et al., 2022 ; Chai et al., 2024 ; Sanusi et al., 2024 ). Accordingly, the behavioral-intention component of the RTAI-BIS was designed with TPB as its conceptual basis. 2.3 Integrating TPACK and TPB: The Conceptual Foundation of the RTAI-BIS TPACK and TPB were used together because readiness to teach AI cannot be understood only as knowledge possession or only as willingness to act. TPACK helps specify what teachers need to know to teach AI, whereas TPB helps explain whether they are disposed to do so within their institutional context. A similar dual-framework strategy has been employed by Habibi et al. ( 2023 ), who integrated TPB with TPACK to model preservice teachers' technology integration intentions and found that the combination explained substantially more variance than either framework alone. The initial item pool was therefore constructed to represent both readiness-related and TPB-informed intention-related dimensions (see Fig. 2 for the theoretical-to-empirical mapping). How the TPB-informed items performed during scale refinement is examined in the Discussion (Section 5.1 ). 3. Methodology 3.1 Research Design This study employed a scale development research design to create a valid and reliable instrument for measuring IT teachers' readiness to teach AI and their behavioral intentions. The scale development process followed established guidelines in the literature (Büyüköztürk, 2002 ; DeVellis, 2017 ; Güngör, 2016 ; Koyuncu & Kılıç, 2019 ) and comprised three major phases: item pool development, validity studies, and reliability studies. The overall scale development procedure is illustrated in Fig. 3 . 3.2 Item Pool Development 3.2.1 Literature Review In the initial phase, national and international AI education curricula (both formal and informal), AI textbooks, and scale items used in related empirical studies were systematically reviewed. This review served to identify the content domain and inform the generation of candidate items aligned with the TPACK and TPB frameworks. 3.2.2 Item Generation Based on the literature review, a preliminary pool of 294 items was generated. The researchers refined this pool by eliminating overlapping or redundant items while ensuring alignment with the theoretical foundations, yielding a draft pool of 52 items for expert review. 3.2.3 Expert Review The 52-item draft was submitted to a panel of six academic experts for content validity evaluation, linguistic appropriateness assessment, and face validity review. Expert panel details are presented in Table 1 . Table 1 Expert Panel Composition Expert Faculty / Department Title 1 Faculty of Education, Computer Education and Instructional Technology Professor 2 Faculty of Education, Computer Education and Instructional Technology Associate Professor 3 Faculty of Education, Computer Education and Instructional Technology Research Assistant (PhD) 4 Faculty of Education, Educational Sciences Professor 5 Faculty of Education, Educational Sciences Associate Professor 6 Faculty of Education, Foreign Languages Education (Language Specialist) Assistant Professor Following expert review, seven items were removed and modifications were applied to selected items based on expert recommendations, resulting in a 45-item candidate scale. 3.2.4 Pilot Testing A pilot study was conducted to further assess face validity. The 45-item candidate scale was converted into a Google Forms questionnaire and administered online to 40 senior preservice IT teachers enrolled at Yıldız Technical University, Department of Computer Education and Instructional Technology. Of the 40 participants, 10 provided detailed and usable written feedback on item clarity and relevance. Based on this feedback, final adjustments were made to the candidate scale, which retained its 45-item structure. Although the pilot sample comprised preservice teachers rather than practicing IT teachers, the pilot's purpose was limited to face validity and item clarity assessment, for which this population was considered appropriate. 3.3 Participants Two independent samples of IT teachers serving in Turkish public and private schools were recruited using non-probability snowball sampling (Table 2 ). Because no centralized registry of IT teachers exists in Turkey, snowball sampling was selected to maximize geographic reach within this dispersed population. Participants responded to the online questionnaire distributed via electronic channels. Across both study phases combined, respondents represented 77 of Turkey's 81 provinces (71 in the EFA sample, 58 in the CFA sample), indicating that the sampling chains extended well beyond localized networks. Study 1 (Exploratory Factor Analysis) A total of 392 IT teachers participated in the EFA phase. Study 2 (Confirmatory Factor Analysis) A total of 258 IT teachers participated in the CFA phase. This sample exceeds the minimum of 200 cases recommended for ML-based CFA (Kline, 2023 ) and provides approximately four observations per freely estimated parameter in the final model (Hair et al., 2019 ). Table 2 Participant Demographics by Study Phase Variable Category Study 1 — EFA (n = 392) Study 2 — CFA (n = 258) n % n % Gender Female 194 49.49 134 51.94 Gender Male 198 50.51 124 48.06 School Type Public School 257 65.56 183 70.93 School Type Private School 135 34.44 75 29.07 Years of Experience 0–5 years 151 38.52 91 35.27 Years of Experience 6–10 years 105 26.79 61 23.64 Years of Experience 11–15 years 57 14.54 45 17.44 Years of Experience 16–20 years 56 14.29 47 18.22 Years of Experience 21 + years 23 5.87 14 5.43 Prior AI Training Yes 199 50.77 134 51.94 Prior AI Training No 193 49.23 124 48.06 Note. The two samples were independently recruited via snowball sampling and did not overlap. The demographic distributions were comparable across the two phases, supporting the suitability of independent-sample cross-validation. 3.4 Data Collection Instrument The candidate scale comprised 45 items. Most items were rated on a 5-point agreement scale (1 = Strongly Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, 5 = Strongly Agree). However, the five AI Interest and Engagement (AIE) items (M41–M45) used a 5-point frequency format (1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, 5 = Very Often), because they were intended to capture behavioral engagement with AI rather than attitudinal endorsement. This mixed response-format design was retained in both study administrations and is considered in interpreting the CFA results (see Section 4.2 ). Items were designed to assess two overarching dimensions: (a) readiness to teach AI, informed by the TPACK framework, and (b) behavioral intentions related to teaching AI, informed by the TPB. Within the TPB-informed dimension, the initial item pool included items operationalizing all three TPB components. These were attitude toward the behavior (e.g., beliefs about the value of AI education for students), subjective norms (e.g., perceived expectations of colleagues and administrators regarding teaching AI), and perceived behavioral control (e.g., self-efficacy beliefs and perceived resource access for AI instruction). The instrument also included a personal information form collecting demographic data. The scale items were originally developed in Turkish. For the purposes of international dissemination, a forward-backward translation procedure aligned with the ITC Guidelines for Translating and Adapting Tests (International Test Commission, 2018 ) was conducted to produce an English version of the scale. 3.5 Data Analysis The study used IBM SPSS v25 for exploratory factor analysis and IBM SPSS AMOS v26 for confirmatory factor analysis. Common Method Bias. Because the study relied on a single self-report instrument administered at one time point, common method bias (CMB) is a potential concern (Podsakoff et al., 2003 ). Rather than relying on Harman's single-factor test, which has been shown to be an insensitive diagnostic (Podsakoff et al., 2003 ), CMB risk was evaluated through converging structural evidence. Specifically, the multi-factor CFA solution, HTMT values below .90, and AVE values above recommended thresholds were collectively examined to assess whether a single-method factor could plausibly account for the observed correlations. Exploratory Factor Analysis (EFA). Prior to factor extraction, the analysis examined item-total correlations to identify items with insufficient discrimination. The Kaiser-Meyer-Olkin (KMO) measure and Bartlett's Test of Sphericity assessed sampling adequacy. The study employed principal component analysis for factor extraction. Principal component analysis was selected for data reduction at the exploratory stage. With communalities consistently above .50 and the majority above .70, PCA and common factor methods such as principal axis factoring are expected to yield convergent solutions (Costello & Osborne, 2005 ; Fabrigar et al., 1999 ). The number of factors was determined based on the Kaiser criterion (eigenvalues > 1) and scree plot analysis. Direct Oblimin rotation was applied given the theoretical expectation of correlated factors. Items were retained based on factor loading thresholds (≥ .40) and the absence of cross-loadings exceeding .10 difference between factors (Koyuncu & Kılıç, 2019 ; Yaşlıoğlu, 2017 ). To verify that the factor structure was not contingent on the extraction method, the analysis was replicated using Principal Axis Factoring (PAF); results are reported in Section 4.1.5 . Confirmatory Factor Analysis (CFA). The factor structure identified through EFA was tested using CFA with the independent sample. CFA was conducted using Maximum Likelihood (ML) estimation. Prior to analysis, univariate and multivariate normality were examined. Univariate skewness values ranged from − 1.97 to − 0.42 and kurtosis values from − 0.75 to 3.23, all within the thresholds recommended by Kline ( 2023 ; |skewness| < 2, |kurtosis| < 7). Although Mardia's multivariate kurtosis was elevated (normalized CR = 55.11), simulation research has demonstrated that ML estimation yields robust parameter estimates with 5-category ordinal indicators and sample sizes exceeding 200 (Rhemtulla et al., 2012 ; Li, 2016 ; Beauducel & Herzberg, 2006 ). To account for potential chi-square inflation, Bollen-Stine bootstrap (500 resamples) was applied (Bollen & Stine, 1992 ). As a further robustness check, the CFA model was re-estimated on a polychoric covariance matrix (Tucker's congruence > .99, confirming ML robustness to ordinal measurement). The study evaluated model fit using multiple indices: RMSEA, SRMR, GFI, NFI, TLI, CFI, IFI, and χ²/df (Hu & Bentler, 1999 ; Kline, 2023 ). A .70 threshold for standardized regression weights (SRW) was adopted as the primary retention criterion, corresponding to approximately 50% shared variance between an item and its factor (Hair et al., 2019 ). This threshold is also consistent with the AVE ≥ .50 convergent-validity requirement (Fornell & Larcker, 1981 ). Re-specification followed a sequential, one-item-at-a-time protocol: after each model estimation, the item with the lowest SRW was identified and evaluated for removal (Koyuncu & Kılıç, 2019 ; Kline, 2023 ). Removal decisions were guided primarily by SRW magnitude but also informed by construct alignment and parsimony; items that weakened the definitional focus of their target factor were removed even when their loading was near the threshold. When successive removal left a factor without a stable set of adequately performing indicators, that factor was not retained in the final model. Convergent validity was assessed through Average Variance Extracted (AVE) values, with .50 as the minimum threshold (Fornell & Larcker, 1981 ; Yaşlıoğlu, 2017 ). Discriminant validity was evaluated using the Heterotrait-Monotrait (HTMT) ratio of correlations, with values below .90 indicating adequate discriminant validity (Henseler et al., 2015 ). Configural invariance was examined by fitting the CFA model separately to gender and school type subgroups. Reliability Analysis. The study assessed internal consistency using Cronbach's alpha (α) for both the overall scale and individual subscales, with .70 as the minimum acceptable threshold (Güngör, 2016 ; Nunnally & Bernstein, 1994 ). Composite Reliability (CR) was computed as CR = (Σλ)² / [(Σλ)² + Σ(1 − λ²)], where λ denotes standardized factor loadings (Fornell & Larcker, 1981 ), with .70 as the minimum threshold (Yaşlıoğlu, 2017 ). McDonald's omega (ω) was additionally computed as a model-based reliability coefficient that does not assume tau-equivalence (Revelle & Zinbarg, 2009 ). Item-total correlations and inter-subscale correlations were also examined. 3.6 Ethical Considerations This study received ethical approval from the Yıldız Technical University Social and Human Sciences Research Ethics Committee (Report No: 20240402851, Decision No: 2024.04; date: April 1, 2024). All participants were informed about the purpose of the study, the voluntary nature of participation, and the confidentiality of their responses. Informed consent was obtained electronically prior to questionnaire completion. No personally identifiable information was collected. 3.7 Data Availability The anonymized dataset generated during this study is available from the corresponding author upon reasonable request. 4. Results 4.1 Exploratory Factor Analysis 4.1.1 Preliminary Analyses Item-total correlations were computed to assess each item's consistency with the overall scale. Four items (M7, M31, M32, M5) demonstrated very low correlation values and were removed from further analyses, leaving 41 items. The Cronbach's alpha coefficient for the remaining 41 items was .976 (alpha was not computed at the initial 45-item stage, as it would have been deflated by items with near-zero correlations). Common Method Bias Assessment. Because all data were collected through a single self-report instrument, CMB was evaluated through converging structural evidence (Podsakoff et al., 2003 ): (a) the EFA distributed variance across five factors rather than one, (b) all HTMT values remained below .90 (see Section 4.2 ), and (c) AVE values exceeded .50 for all factors. These results suggest that common method variance does not provide a plausible single-factor explanation for the observed correlations; procedural remedies for future administrations are discussed in Section 5.5 . The Kaiser-Meyer-Olkin (KMO) test and Bartlett's Test of Sphericity were conducted to evaluate the suitability of the data for factor analysis (Table 3 ). Table 3 KMO and Bartlett's Test of Sphericity Results Test Value KMO Measure of Sampling Adequacy .966 Bartlett's Test of Sphericity — Approx. χ² 17437.946 Bartlett's Test of Sphericity — df 820 Bartlett's Test of Sphericity — p < .001 Note. N = 392. Both indices confirmed that the data were suitable for factor analysis. The KMO value (.966) well exceeded the recommended .60 threshold, and Bartlett's test was statistically significant (Büyüköztürk, 2002 ; Koyuncu & Kılıç, 2019 ). 4.1.2 Factor Extraction Principal Component Analysis was conducted with the remaining 41 items. The Kaiser criterion identified five factors with eigenvalues greater than 1.0 (Table 4 ). The scree plot analysis corroborated this five-factor solution. Table 4 Eigenvalues and Explained Variance for the First 10 Components Component Eigenvalue % of Variance Cumulative % 1 21.274 51.887 51.887 2 4.058 9.896 61.784 3 1.996 4.869 66.653 4 1.711 4.173 70.827 5 1.216 2.966 73.793 6 0.948 2.313 76.105 7 0.752 1.835 77.940 8 0.674 1.645 79.584 9 0.625 1.524 81.109 10 0.573 1.399 82.507 The scree plot (Fig. 4 ) visually confirmed the five-factor solution, with a clear inflection point after the fifth component. Communality values for all 41 items were above .50, indicating that the extracted factors adequately accounted for the variance in each item (Erkuş, 2019 ; Yaşlıoğlu, 2017 ). 4.1.3 Factor Rotation and Item Retention The unrotated component matrix was examined, and four items (M6, M8, M13, M15) that did not meet the retention criteria (factor loading ≥ .40 on any factor, or cross-loading difference < .10) were removed. Direct Oblimin oblique rotation was applied to the remaining 37 items, based on the theoretical expectation of inter-factor correlations. The rotated component matrix is presented in Table 5 . Table 5 Rotated Component Matrix (Direct Oblimin) — Factor Loadings Item Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 M28 .889 M23 .882 M26 .851 M27 .848 M24 .831 M29 .823 M30 .800 M25 .789 M22 .785 M18 .762 M17 .759 M20 .708 M19 .670 M16 .649 M36 .925 M37 .898 M35 .870 M34 .854 M38 .845 M39 .830 M33 .822 M40 .785 M43 .839 M42 .820 M44 .763 M45 .733 M41 .719 M3 .736 M2 .734 M4 .726 M1 .699 M9 .469 M14 −.709 M10 −.681 M12 −.664 M21 −.662 M11 −.478 Note. Only primary factor loadings are displayed. Factor loadings < .40 are suppressed for clarity. Negative loadings on Factor 5 reflect the oblique rotation direction and do not indicate reverse-scored items. Table 6 Inter-Factor Correlation Matrix F1 F1 F2 F3 F4 F5 1.000 F2 .517 1.000 F3 .506 .315 1.000 F4 .377 .475 .273 1.000 F5 −.555 −.317 −.299 −.202 1.000 The inter-factor correlations (Table 6 ) confirmed the appropriateness of the oblique rotation method. 4.1.4 EFA Summary The EFA yielded a five-factor structure with 37 items, explaining 73.79% of the total variance (Table 7 ). Factor labels were assigned based on the theoretical relationships of the items within each factor. This solution was treated as a candidate structure; full reliability analyses were conducted on the CFA-confirmed model (see Section 4.3 ). Table 7 EFA Factor Structure Summary Factor Label No. of Items Item Codes % Variance 1 Technological, Pedagogical, and Content Knowledge for Teaching AI (TPACK-TAI) 14 M16, M17, M18, M19, M20, M22, M23, M24, M25, M26, M27, M28, M29, M30 51.887 2 Attitude Toward AI (ATA) 8 M33, M34, M35, M36, M37, M38, M39, M40 9.896 3 AI Interest and Engagement (AIE) 5 M41, M42, M43, M44, M45 4.869 4 Disposition Toward Teaching AI (DTAI) 5 M1, M2, M3, M4, M9 4.173 5 Technological Knowledge for Teaching AI (TK-TAI) 5 M10, M11, M12, M14, M21 2.966 Total 37 73.793 4.1.5 Extraction Method Robustness Check To confirm that the factor structure was not an artifact of the extraction method, the EFA was replicated using Principal Axis Factoring (PAF) with Direct Oblimin rotation. The PAF solution recovered the same five-factor structure with identical item-to-factor assignments. Tucker's congruence coefficients between the PCA and PAF pattern matrices ranged from .92 to .99, exceeding the .85 threshold for fair similarity (Lorenzo-Seva & ten Berge, 2006 ). The PAF solution explained 61.07% of total variance, consistent with the expected reduction when modeling common variance only (Costello & Osborne, 2005 ; Fabrigar et al., 1999 ). 4.2 Confirmatory Factor Analysis The five-factor, 37-item structure from EFA was tested with the independent CFA sample (n = 258) using IBM SPSS AMOS v26. Item retention followed the sequential re-specification procedure described in Section 3.5 . At each step, the item with the lowest SRW was identified, evaluated for both statistical performance and substantive alignment with the target construct, and removed before re-estimating the model. This process was repeated until all remaining items met the .70 threshold and model fit indices reached acceptable levels. Across successive re-specifications, a total of 11 items were removed in the following order: M4, M44, M45, M41, M42, M43, M39, M9, M34, M16, and M19. Table 8 summarizes the removed items, their original EFA factor assignments, and the removal rationale. Table 8 Items Removed During CFA Re-Specification Order Item Original EFA Factor SRW at Removal Primary Removal Basis 1 M4 DTAI (F4) .531 Low loading; less aligned with retained DTAI core 2 M44 AIE (F3) .594 Low loading; personal AI-use behavior; frequency format 3 M45 AIE (F3) .584 Low loading; personal AI-use behavior; frequency format 4 M41 AIE (F3) .625 Low loading; personal AI-use behavior; frequency format 5 M42 AIE (F3) .963 Personal AI-use behavior; frequency format; factor collapsing 6 M43 AIE (F3) .924 Final AIE indicator; factor eliminated 7 M39 ATA (F2) .919 Content overlap with retained ATA items 8 M9 DTAI (F4) .767 Conditional willingness; less aligned with retained DTAI core 9 M34 ATA (F2) .916 Peripheral to retained ATA definition 10 M16 TPACK-TAI (F1) .722 Broader AI knowledge; less teaching-specific 11 M19 TPACK-TAI (F1) .734 Broader societal awareness; less teaching-specific Note. SRW = standardized regression weight at the model estimation step immediately preceding the item's removal. Items 1–4 fell below the .70 threshold. Items 5–11 were removed primarily on conceptual grounds, as detailed below. The removals followed three patterns: (a) all five AIE items (M41–M45) measured personal AI tool-use frequency rather than teaching-specific capability, and their frequency response format introduced heterogeneity (Podsakoff et al., 2003 ), leading to elimination of the AIE factor; (b) M4 and M9 originated from TPB components whose content diverged from the retained DTAI core; and (c) the remaining items (M34, M39, M16, M19) reflected diffuse evaluations or broad knowledge peripheral to the target constructs. Theoretical implications of these patterns are discussed in Section 5.1 . The remaining four factors retained their conceptual coherence and all item SRWs in the final 26-item model exceeded .70 (see Table 13 ). Because the AIE factor was eliminated, the surviving factors were renumbered: TK-TAI moved from EFA Factor 5 to CFA Factor 3, and DTAI moved from EFA Factor 4 to CFA Factor 4. Model fit was further improved by examining modification indices. The largest modification index indicated a covariance between the error terms of items M20 ("I can explain how AI applications work using examples") and M22 ("I can explain the strengths and weaknesses of AI technology"), both of which loaded on the TPACK-TAI factor and shared overlapping content related to AI content knowledge. This single error covariance was specified based on both statistical and substantive grounds. No further modifications were warranted, as remaining modification indices did not indicate theoretically justifiable re-specifications. The final model (χ² = 759.910, df = 292, p < .001) is presented in Fig. 5 , and model fit indices are summarized in Table 9 . Table 9 CFA Model Fit Indices Index Value Acceptable Threshold Reference χ²(292) 759.910, p < .001 — — χ²/df 2.602 < 5 Güngör ( 2016 ) RMSEA .079, 90% CI [.072, .086] ≤ .08 Koyuncu & Kılıç ( 2019 ) SRMR .061 ≤ .08 Yaşlıoğlu ( 2017 ) GFI .810 ≥ .90 Güngör ( 2016 ) NFI .911 ≥ .90 Koyuncu & Kılıç ( 2019 ) TLI .937 ≥ .90 Yaşlıoğlu ( 2017 ) CFI .943 ≥ .90 Güngör ( 2016 ) IFI .943 ≥ .90 Koyuncu & Kılıç ( 2019 ) All primary model fit indices (CFI, TLI, RMSEA, SRMR) were within acceptable ranges, supporting the construct validity of the four-factor, 26-item structure. The RMSEA point estimate (.079) fell within the adequate-fit range, and its 90% confidence interval [.072, .086] indicated that the upper bound remained below .10, reinforcing the acceptability of the model fit. GFI (.810) fell below .90 but is no longer recommended as a primary fit index (Hu & Bentler, 1999 ; Sharma et al., 2005 ); model adequacy rests on the primary indices above. Bollen-Stine bootstrap (500 resamples) similarly yielded p < .001, consistent with the known sensitivity of χ² to sample size. Table 10 Average Variance Extracted (AVE) Values Factor AVE Factor 1 — TPACK-TAI .756 Factor 2 — ATA .831 Factor 3 — TK-TAI .700 Factor 4 — DTAI .840 Scale Average .782 All AVE values exceeded the .50 threshold, indicating adequate convergent validity (Yaşlıoğlu, 2017 ). Discriminant validity was further evaluated using the Heterotrait-Monotrait (HTMT) ratio of correlations (Henseler et al., 2015 ). HTMT values are presented in Table 11 . Table 11 HTMT Discriminant Validity Matrix TPACK-TAI TPACK-TAI ATA TK-TAI DTAI — ATA .590 — TK-TAI .864 .537 — DTAI .503 .799 .471 — All HTMT values were below the conservative .90 threshold, supporting discriminant validity across all factor pairs. The highest HTMT value was observed between TPACK-TAI and TK-TAI (.864), which is theoretically expected given that both factors capture knowledge-based dimensions of the TPACK framework. All remaining HTMT values were below the stricter .85 criterion. 4.2.1 CFA Summary The CFA confirmed a four-factor structure with 26 items. The final factor structure is presented in Table 12 . Table 12 Final Scale Structure After CFA Factor Label No. of Items Item Codes Std. Regression Weights 1 Technological, Pedagogical, and Content Knowledge for Teaching AI (TPACK-TAI) 12 M17, M18, M20, M22, M23, M24, M25, M26, M27, M28, M29, M30 .705–.945 2 Attitude Toward AI (ATA) 6 M33, M35, M36, M37, M38, M40 .897–.927 3 Technological Knowledge for Teaching AI (TK-TAI) 5 M10, M11, M12, M14, M21 .823–.857 4 Disposition Toward Teaching AI (DTAI) 3 M1, M2, M3 .911–.924 Total 26 Although DTAI retained only three items, this meets the minimum identification requirement for CFA (Kline, 2023 ). The retained items demonstrated uniformly high SRWs (.911–.924), strong AVE (.840), and high composite reliability (.930), reflecting a coherent construct core: evaluative beliefs about the importance of AI education for students' futures. The retention of both TPACK-TAI and TK-TAI as separate factors indicates that integrated instructional readiness and narrower technology-specific capability represent empirically distinguishable dimensions within this sample. The HTMT value between the two factors (.864) remained below the .90 discriminant validity threshold (see Table 11 ), supporting this distinction. Table 13 Individual Standardized Regression Weights from Confirmatory Factor Analysis Factor Item SRW Factor Item SRW TPACK-TAI M17 .763 ATA M33 .906 TPACK-TAI M18 .705 ATA M35 .925 TPACK-TAI M20 .804 ATA M36 .902 TPACK-TAI M22 .834 ATA M37 .927 TPACK-TAI M23 .919 ATA M38 .913 TPACK-TAI M24 .928 ATA M40 .897 TPACK-TAI M25 .945 TK-TAI M10 .832 TPACK-TAI M26 .903 TK-TAI M11 .823 TPACK-TAI M27 .933 TK-TAI M12 .857 TPACK-TAI M28 .893 TK-TAI M14 .825 TPACK-TAI M29 .890 TK-TAI M21 .847 TPACK-TAI M30 .884 DTAI M1 .914 DTAI M2 .924 DTAI M3 .911 Note. All standardized regression weights exceed the .70 retention threshold. Values extracted from IBM SPSS AMOS v26 output. 4.2.2 Configural Invariance To assess the structural stability of the four-factor model across demographic subgroups, configural invariance was examined by fitting the CFA model separately to gender and school type groups (Table 14 ). Table 14 Configural Invariance: Model Fit by Subgroup Grouping Variable Group n χ²/df CFI RMSEA Gender Female 134 2.266 .906 .098 Gender Male 124 2.090 .930 .094 School Type Public 183 2.384 .931 .087 School Type Private 75 1.936 .898 .112 Note. RMSEA 90% confidence intervals and SRMR values are not reported for subgroup models because AMOS does not produce stable CI estimates when models are fitted to small samples (n < 200) with complex structures (Kenny et al., 2015 ). SRMR for the full-sample model was .061 (Table 9 ). The four-factor structure demonstrated acceptable fit across both gender groups (CFI ≥ .90, χ²/df < 3.0), supporting configural invariance for gender. Elevated RMSEA values in the subgroups are attributable to reduced sample sizes (Kenny et al., 2015 ). For school type, the public school subsample showed acceptable fit; the private school subsample (n = 75) showed marginally below-threshold CFI (.898), likely attributable to the small subsample size. Overall, these results provide preliminary evidence that the four-factor structure holds across the examined subgroups. Full metric and scalar invariance testing with larger subsamples is recommended for future research. 4.3 Reliability Analysis 4.3.1 Item-Total Correlations Corrected item-total correlations for the 26-item scale ranged from .731 to .920 (Table 15 ), indicating that all items were strong representatives of their respective subscales. Table 15 Corrected Item-Total Correlations Subscale Item r Subscale Item r TPACK-TAI M17 .780 TK-TAI M10 .785 TPACK-TAI M18 .731 TK-TAI M11 .779 TPACK-TAI M20 .831 TK-TAI M12 .824 TPACK-TAI M22 .850 TK-TAI M14 .799 TPACK-TAI M23 .897 TK-TAI M21 .792 TPACK-TAI M24 .905 ATA M33 .888 TPACK-TAI M25 .920 ATA M35 .907 TPACK-TAI M26 .872 ATA M36 .883 TPACK-TAI M27 .909 ATA M37 .911 TPACK-TAI M28 .871 ATA M38 .893 TPACK-TAI M29 .867 ATA M40 .879 TPACK-TAI M30 .865 DTAI M1 .866 DTAI M2 .891 DTAI M3 .868 4.3.2 Inter-Subscale and Scale-Total Correlations Table 16 Inter-Subscale and Scale-Total Correlations TPACK-TAI TPACK-TAI ATA TK-TAI DTAI Scale Total 1.000 .940 ATA .572 1.000 .783 TK-TAI .818 .509 1.000 .863 DTAI .482 .762 .440 1.000 .690 Note. All correlations are significant at p < .01 (two-tailed). Inter-subscale correlations (Table 16 ) ranged from .440 to .818, and subscale-to-total correlations ranged from .690 to .940, confirming that all subscales were significantly and highly correlated with each other and with the overall scale. 4.3.3 Internal Consistency Table 17 Cronbach's Alpha, Composite Reliability, and McDonald's Omega Values Scale / Subscale No. of Items Cronbach's α Composite Reliability (CR) McDonald's ω Factor 1 — TPACK-TAI 12 .974 .967 .977 Factor 2 — ATA 6 .967 .930 .973 Factor 3 — TK-TAI 5 .921 .954 .941 Factor 4 — DTAI 3 .939 .930 .961 Overall Scale 26 .973 .986 .975 Internal consistency was uniformly strong. All Cronbach's α, CR, and McDonald's ω values (Table 17 ) exceeded the .70 threshold across both the overall scale and all subscales (Güngör, 2016 ; Revelle & Zinbarg, 2009 ; Yaşlıoğlu, 2017 ). McDonald's omega, which accounts for the congeneric measurement model and does not assume tau-equivalence, confirmed the robustness of these estimates. For TPACK-TAI, the notably high alpha (.974) is accompanied by an inter-item correlation mean of .758 (range = .592–.895), indicating a cohesive factor rather than item redundancy. 5. Discussion This study developed and validated the Readiness to Teach AI and Behavioral Intention Scale (RTAI-BIS) for K-12 IT teachers. The discussion focuses on what the final structure means theoretically, how the scale relates to prior work, and where its present evidential limits remain. 5.1 Factor Structure and Theoretical Alignment The final RTAI-BIS comprises 26 items organized into four factors, indicating that readiness to teach AI is best represented as a combination of knowledge-related readiness and intention-related orientation rather than a single disposition. This supports the use of TPACK as a framework for the readiness component, consistent with prior work arguing that AI instruction requires domain-specific integration of content, pedagogy, and technology (Kim et al., 2021a ; Yue et al., 2024 ). The separation between TPACK-TAI and TK-TAI suggests that teachers distinguish between integrated instructional readiness — encompassing explanation, lesson design, assessment, and pedagogical adaptation — and narrower technology-specific capability centered on AI tools, software environments, and coding platforms. This interpretation is consistent with item content: TK-TAI items concern use of specific tools and troubleshooting, whereas TPACK-TAI items emphasize concept explanation, instructional planning, and the coordination of pedagogy with technology. The two factors are theoretically related, as TK is embedded within the broader TPACK framework (Mishra & Koehler, 2006 ), yet they are empirically non-redundant (HTMT = .864), indicating that tool-level competence and integrated pedagogical-content readiness remain distinguishable in this context. This finding resonates with Velander et al. ( 2024 ), who reported that Swedish K-12 teachers' AI content knowledge was largely acquired through incidental exposure and often contained misconceptions, underscoring that technological familiarity and pedagogical-content readiness develop along different trajectories. The TPB-informed component of the scale is more nuanced. DTAI most closely reflects attitude toward the behavior, because its items concern whether AI education should be provided to students. ATA, by contrast, reflects evaluations of AI as an object or societal phenomenon rather than direct commitment to classroom enactment. The strong ATA-DTAI correlation nevertheless suggests that favorable views of AI and favorable views of teaching AI are empirically connected, even when they are not identical constructs. Habibi et al. ( 2023 ) reported a similar pattern: attitudinal beliefs were stronger predictors of preservice teachers' technology integration intentions than knowledge variables alone. In a complementary finding, Addo and Sentance ( 2023 ) used self-determination theory to explore K-12 teachers' motivation for teaching AI and found that intrinsic interest in AI, perceived competence, and institutional support were key motivational drivers. These drivers correspond to the ATA and DTAI dimensions of the RTAI-BIS, reinforcing the view that dispositional and evaluative orientations play a central role in shaping teaching behavior. A central interpretive issue concerns why subjective norms and perceived behavioral control did not survive as independent factors. Subjective norm variance clustered with the dispositional teaching factor, while perceived behavioral control items overlapped with knowledge-related factors — consistent with evidence that normative and control beliefs often merge with attitudinal and capability constructs in teaching contexts (Ajzen, 2002 ; Armitage & Conner, 2001 ; Bandura, 1997 ; Cheung & Cheung Tse, 2021 ). This indicates that some TPB components are latently embedded in broader readiness and disposition constructs rather than manifesting as stand-alone subscales. The shift from five EFA factors to four CFA factors reinforces this interpretation. The removed AI Interest and Engagement factor captured personal engagement with AI tools — behaviors that reflect how often teachers use AI in their own lives rather than whether they are prepared to teach it. Because such personal-use behaviors are likely too transient and idiosyncratic to constitute a stable component of teaching readiness, the factor did not replicate under confirmatory conditions. A response-format difference may also have contributed, as the AIE items used a frequency scale rather than the Likert agreement format of all other subscales (Podsakoff et al., 2003 ). The retained four-factor model is more tightly aligned with the study's construct focus: teaching-related capability, technology-related capability, attitudinal orientation, and dispositional commitment. The scale therefore captures not general AI enthusiasm but readiness and willingness to teach it. The transition from five EFA factors to four CFA factors reflects cross-sample refinement rather than fit maximization. The .70 SRW threshold was set a priori, and each removal was evaluated for conceptual as well as statistical justification (see Table 8 ). Crucially, the AIE dimension was proposed by the EFA sample and independently tested by the CFA sample; its failure to replicate is a genuine cross-validation outcome. That the surviving model produced uniformly high AVE values (.700–.840), clean discriminant validity (all HTMT < .90), and configural invariance across subgroups provides converging evidence against post-hoc overfitting. 5.2 Psychometric Properties The psychometric results indicate that the final instrument is internally coherent and structurally consistent across independent samples. The EFA and CFA results converged on a structure with interpretable factors, acceptable model fit, and strong item-factor relations. Convergent validity was supported by AVE values above recommended thresholds, and discriminant validity was supported by HTMT values below the conservative .90 criterion (Henseler et al., 2015 ). The highest HTMT value occurred between TPACK-TAI and TK-TAI, which is theoretically expected because both factors concern knowledge-related readiness; importantly, the value still remained below the threshold, suggesting related but distinguishable constructs. Reliability evidence was similarly strong. Cronbach's alpha, composite reliability, and McDonald's omega all supported high internal consistency for the total scale and subscales, while item-total correlations indicated that the retained items contributed meaningfully to their respective dimensions. The notably high alpha of TPACK-TAI (.974) warrants comment: with 12 items and corrected item-total correlations ranging from .731 to .920, this value reflects a cohesive factor rather than item redundancy, yet it suggests that a shorter form may be feasible without substantial information loss. Future studies should evaluate whether a reduced TPACK-TAI subscale can maintain comparable validity and reliability. Configural invariance results also suggest that the four-factor model is structurally plausible across gender and school type groups, although stronger subgroup evidence is still needed before broader invariance claims are made. Given these results, the RTAI-BIS is suitable for research and diagnostic use at the current stage of validation. The retained factor structure appears sufficiently coherent to support substantive interpretation rather than only exploratory description. Although a dedicated criterion-validation design was not part of the present study, the broader dataset provides limited external-pattern evidence. Teachers who had completed prior AI training scored significantly higher on TPACK-TAI (t(256) = 4.32, p < .001, d = 0.54) and TK-TAI (t(256) = 5.11, p < .001, d = 0.64), with the stronger differentiation on TK-TAI consistent with the expectation that technological knowledge is more directly shaped by formal training. These patterns constitute preliminary known-groups evidence but do not substitute for a formal criterion-validation study based on external performance measures. 5.3 Comparison with Existing Literature As noted in Section 1 , prior work has approached readiness and intention separately. The RTAI-BIS addresses this gap by integrating TPACK-based competence assessment with TPB-informed intention measurement in a single validated instrument (cf. Ayanwale et al., 2022 ; Chai et al., 2024 ; Kim et al., 2021a ; Yue et al., 2024 ). The distinction from adjacent AI-related instruments is equally important. Several recent scales measure teachers' competence in using AI as a pedagogical tool, or their general AI self-efficacy, rather than their readiness to teach AI as curricular content (Celik, 2023 ; Chiu et al., 2025 ; Wang & Chuang, 2024 ). The closest comparable instrument, the TAAI (Guo et al., 2025 ), assesses teachers' acceptance of AI in education through TAM-based dimensions including perceived usefulness, ease of use, self-efficacy, and anxiety; however, acceptance of AI as an educational resource is conceptually distinct from readiness and intention to teach AI as a subject, which additionally requires pedagogical-content integration and dispositional commitment to classroom enactment. The present scale accordingly addresses this more specific implementation problem: whether teachers are prepared and inclined to teach AI itself. This specificity is important given cross-national evidence that even teachers with moderate AI awareness report feeling unprepared to adapt AI curricula to classroom contexts (Du et al., 2023 ). In the Turkish context, this is particularly relevant because policy commitments to AI education already exist, but validated tools for assessing teacher preparedness have been lacking (T.C. CBDDO, 2021; T.C. MEB, 2023). Cross-national studies support the multidimensional nature of the construct. Yau et al. ( 2023 ) identified six qualitatively distinct categories in how teachers conceptualize teaching AI, aligning with the RTAI-BIS's separation of knowledge-related and intention-related dimensions. Jatileni et al. ( 2024 ) found that Namibian teachers' intention to teach AI depended on attitude and confidence rather than on readiness alone, while Ayanwale and Sanusi ( 2023 ) reported significant STEM vs. non-STEM group differences that underscore the importance of domain-specific measurement. Within Turkey, although intention-based scale research has effectively captured teaching readiness for emerging domains (Günbatar & Bakırcı, 2019 ), no analogous instrument existed for AI — a gap the RTAI-BIS now addresses. 5.4 Practical Implications Practically, the RTAI-BIS can function as a diagnostic tool for curriculum implementation. School leaders and teacher educators can use it to identify whether weak readiness stems more from pedagogical-content capability, technology-related capability, or attitudinal-dispositional barriers. Consider two contrasting profiles: a teacher scoring high on TPACK-TAI and TK-TAI but low on DTAI has the knowledge to teach AI yet lacks the dispositional commitment to do so — an implementation gap that training alone cannot close. Conversely, a teacher scoring high on DTAI and ATA but low on TPACK-TAI is willing and favorably disposed but lacks the pedagogical-content readiness — a training gap amenable to structured professional development. Teacher education providers can use such profiles to design targeted preservice and in-service support rather than treating AI preparation as a single undifferentiated training need. 5.5 Limitations and Future Directions No single validation study is fully definitive. Several limitations should be considered. The study was conducted only with IT teachers in Turkey, so the findings cannot yet be generalized across countries, school systems, or teacher populations. Sampling also introduces a concern: non-probability snowball sampling may have introduced selection bias. Relatedly, the instrument relies on self-report data and thus cannot be assumed to reflect actual classroom practice. As reported in Section 4.1.1 , converging structural evidence mitigated common method bias concerns; however, future studies should employ procedural remedies such as temporal separation or multi-source data collection (Podsakoff et al., 2003 ). Temporal stability was not assessed; future studies should estimate test-retest reliability within a 2–4 week window. The transition from five EFA factors to four CFA factors is both a limitation and a finding: the initial item pool's coverage of personal AI engagement did not survive cross-sample validation, likely because personal tool-use behaviors occupy a different construct space from teaching readiness and because the frequency-scaled items introduced response-format heterogeneity. Future iterations might re-approach the engagement dimension with items anchored to teaching contexts and a consistent response format. Invariance evidence is preliminary because subgroup sizes, especially for private-school teachers, were limited. On the theoretical side, although the initial pool represented all three TPB components, subjective norms and perceived behavioral control did not remain as independent subscales, limiting the instrument's ability to test the full TPB pathway directly. Finally, the study did not include a dedicated criterion-validation design. The preliminary known-groups evidence reported in Section 5.2 should not be treated as a substitute for criterion-related validation based on classroom observation, lesson quality, or prospective implementation outcomes. 6. Conclusion This study produced the RTAI-BIS, a four-factor, 26-item instrument that jointly captures TPACK-based readiness and TPB-informed behavioral intention for teaching AI at the K-12 level. The cross-validated factor structure and strong reliability evidence position the scale as a potentially useful diagnostic tool whose multidimensional profile can distinguish knowledge gaps from attitudinal barriers, informing more targeted approaches to teacher preparation rather than uniform training (see Section 5.4 for profiling applications). The availability of Turkish and English versions facilitates future cross-cultural validation and comparison studies as AI education expands internationally. Knowledge dimensions (TPACK-TAI, TK-TAI) account for the largest share of explained variance, yet attitudinal and dispositional factors remain empirically distinct and necessary — suggesting that teacher readiness for AI education may be knowledge-dominant but not knowledge-sufficient, a pattern that warrants confirmation in future samples. The findings should be interpreted in light of certain limitations, including the single-country sample, reliance on self-report data, and the absence of test-retest evidence. Several directions deserve priority. Criterion-related validity studies should examine whether RTAI-BIS scores predict observable teaching behaviors and implementation quality. Longitudinal administrations can establish score stability and track how readiness shifts after professional development. Replication across teacher populations outside Turkey and beyond IT teachers will clarify the instrument's generalizability. As AI education moves from curriculum documents to classroom practice, the gap between intended instruction and teacher capacity will shape what students actually learn. The RTAI-BIS offers one empirically grounded way to measure that gap. Declarations Funding: The authors did not receive support from any organization for the submitted work. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Ethics Approval: This study received ethical approval from the Yıldız Technical University Social and Human Sciences Research Ethics Committee (Report No: 20240402851, Decision No: 2024.04; date: April 1, 2024). Informed Consent: Informed consent was obtained electronically from all participants prior to questionnaire completion. No personally identifiable information was collected. Data Availability: The anonymized dataset generated during this study is available from the corresponding author upon reasonable request. 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Computers Education: Artif Intell 4:100145. https://doi.org/10.1016/j.caeai.2023.100145 Sanusi IT, Ayanwale MA, Chiu TKF (2024) Investigating the moderating effects of social good and confidence on teachers' intention to prepare school students for artificial intelligence education. Educ Inform Technol 29(1):273–295. https://doi.org/10.1007/s10639-023-12250-1 Sanusi IT, Oyelere SS, Vartiainen H, Suhonen J, Tukiainen M (2023) A systematic review of teaching and learning machine learning in K-12 education. Educ Inform Technol 28:5967–5997. https://doi.org/10.1007/s10639-022-11416-7 Scherer R, Siddiq F, Tondeur J (2019) The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Comput Educ 128:13–35. https://doi.org/10.1016/j.compedu.2018.09.009 Sharma S, Mukherjee S, Kumar A, Dillon WR (2005) A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. J Bus Res 58(7):935–943. https://doi.org/10.1016/j.jbusres.2003.10.007 Shulman LS (1986) Those who understand: Knowledge growth in teaching. Educational Researcher 15(2):4–14. https://doi.org/10.2307/1175860 Shulman LS (1987) Knowledge and teaching: Foundations of the new reform. Harv Educational Rev 57(1):1–22. https://doi.org/10.17763/haer.57.1.j463w79r56455411 Su J, Guo K, Chen X, Chu SKW (2024) Teaching artificial intelligence in K-12 classrooms: A scoping review. Interact Learn Environ 32(9):5207–5226. https://doi.org/10.1080/10494820.2023.2212706 Su J, Zhong Y, Ng DTK (2022) A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers Education: Artif Intell 3:100065. https://doi.org/10.1016/j.caeai.2022.100065 T.C. Cumhurbaşkanlığı Dijital Dönüşüm Ofisi [CBDDO]. (2021) Ulusal yapay zeka stratejisi 2021–2025. https://cbddo.gov.tr/SharedFolderServer/Genel/File/TR-UlusalYZStratejisi2021-2025.pdf T.C. Millî Eğitim Bakanlığı [MEB]. (2023) Yapay zekâ uygulamaları dersi öğretim programı. https://mufredat.meb.gov.tr/Dosyalar/2023112493011132-23174117_yapayzekauygulamalaridersiogretimprogrami_3.23.pdf Touretzky DS, Gardner-McCune C, Martin F, Seehorn D (2019) Envisioning AI for K-12: What should every child know about AI? Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795 UNESCO (2021) AI and education: Guidance for policy-makers. UNESCO Publishing. https://doi.org/10.54675/PCSP7350 Velander J, Taiye MA, Otero N, Milrad M (2024) Artificial intelligence in K-12 education: Eliciting and reflecting on Swedish teachers' understanding of AI and its implications for teaching & learning. Educ Inform Technol 29(4):4085–4105. https://doi.org/10.1007/s10639-023-11990-4 Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540 Wang Y-Y, Chuang Y-W (2024) Artificial intelligence self-efficacy: Scale development and validation. Educ Inform Technol 29:4535–4561. https://doi.org/10.1007/s10639-023-12015-w Yaşlıoğlu MM (2017) Sosyal bilimlerde faktör analizi ve geçerlilik: Keşfedici ve doğrulayıcı faktör analizlerinin kullanılması. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 46(özel sayı), 74–85 Yau KW, Chai CS, Chiu TKF, Meng H, King I, Yam Y (2023) A phenomenographic approach on teacher conceptions of teaching artificial intelligence (AI) in K-12 schools. Educ Inform Technol 28(1):1041–1064. https://doi.org/10.1007/s10639-022-11161-x Yue M, Jong MSY, Ng DTK (2024) Understanding K-12 teachers' technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Educ Inform Technol 29(13):16951–16976. https://doi.org/10.1007/s10639-024-12621-2 Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9291938","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615944229,"identity":"e5f01457-30bb-4c6c-bff1-eadb667772aa","order_by":0,"name":"Hasan Tokatlı","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3RPQrCMBTA8VeEujyIYwqiV3hQ8AMEr9IS0MXB0Ukqgl16AAcv4e4QCTh5gI66OHVxU3AwKYKTNaNg/ktCyY+8EgCX6wcjgJpeesjAW0gk8y36QnxDIo5B4iVvIi0IkD4lESxIt71UdL/xZpiLRBbTObD6hGrX3WfSX/kizvRgnTxO9ht9aZAVBPJSMdgBQ4kvopAQKJ9oUjGZIfuHJuG6JByGNkSYW4iXhID4VzISYXPEkR/P5l8ivblM5bGKKKWCYjAYsnR8PhWPeYulYnuaVZB3jahcyqexAgDM8pzL5XL9X0/OY1HVZvDMJwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9231-4191","institution":"Department of Informatics, Yıldız Technical University, Istanbul, Türkiye","correspondingAuthor":true,"prefix":"","firstName":"Hasan","middleName":"","lastName":"Tokatlı","suffix":""},{"id":615944230,"identity":"6fe176ec-7fbc-479a-967d-667ae05d8d2f","order_by":1,"name":"M. Fatih Erkoç","email":"","orcid":"https://orcid.org/0000-0002-8278-2805","institution":"Department of Computer Education and Instructional Technology, Faculty of Education, Yıldız Technical University, Istanbul, Türkiye","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Fatih","lastName":"Erkoç","suffix":""}],"badges":[],"createdAt":"2026-04-01 12:37:48","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9291938/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9291938/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105979638,"identity":"47942226-7ed9-485e-a063-e081a0fd2f04","added_by":"auto","created_at":"2026-04-02 06:30:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":686907,"visible":true,"origin":"","legend":"\u003cp\u003eThe TPACK framework and its knowledge components (adapted from Koehler \u0026amp; Mishra, 2009). TK = Technological Knowledge; PK = Pedagogical Knowledge; CK = Content Knowledge; TPK = Technological Pedagogical Knowledge; TCK = Technological Content Knowledge; PCK = Pedagogical Content Knowledge; TPACK = Technological Pedagogical Content Knowledge. Reproduced by permission of the publisher, © 2012 by tpack.org\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9291938/v1/e2d17f337f8c16e2539b8411.png"},{"id":105979639,"identity":"054a5c9c-7d81-4b9b-adb5-70e938d37ea8","added_by":"auto","created_at":"2026-04-02 06:30:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5744505,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical-to-empirical mapping of TPB and TPACK constructs in the RTAI-BIS. Panel (a) shows the original 45-item candidate pool with all three TPB components represented. Panel (b) shows the final 26-item structure after EFA/CFA refinement. Subjective norm (M4) and behavioral intention (M9) items loaded on the DTAI factor during EFA but subsequently removed in CFA for failing the SRW threshold. PBC items (M6–M8) were eliminated during EFA for failing loading criteria\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9291938/v1/3543c13d34274d26eb5d2d1e.png"},{"id":105979642,"identity":"01d87326-0f92-4149-87b0-632fff1ba2ae","added_by":"auto","created_at":"2026-04-02 06:30:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5659149,"visible":true,"origin":"","legend":"\u003cp\u003eScale development procedure for the RTAI-BIS, from literature review and item generation through expert review, pilot testing, exploratory and confirmatory factor analyses, and reliability assessment\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9291938/v1/835b8e05d3e6d04b1998c778.png"},{"id":105979641,"identity":"2f1de890-fa14-4332-9ec9-f555ddadee66","added_by":"auto","created_at":"2026-04-02 06:30:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2195426,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot for the 41-item EFA. The arrow indicates the inflection point after the fifth component, supporting a five-factor solution\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9291938/v1/a1b90c1dda5233fd2ed485d2.png"},{"id":105979640,"identity":"03cb855f-5dde-4e6f-9bb6-9421a9555a34","added_by":"auto","created_at":"2026-04-02 06:30:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9134309,"visible":true,"origin":"","legend":"\u003cp\u003eConfirmatory factor analysis path diagram for the four-factor, 26-item RTAI-BIS model. Standardized regression weights are displayed on paths. Inter-factor correlations shown are CFA standardized latent estimates. Error covariance between e9 and e10 (M20 ↔ M22) is specified\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9291938/v1/b4b7e351b39eeda137b46f04.png"},{"id":106093545,"identity":"65e59cbd-5160-4cf6-84b9-518b252109e0","added_by":"auto","created_at":"2026-04-03 11:37:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27059066,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9291938/v1/50ceb4e6-6a5c-4666-b8e1-62a1a9ff3275.pdf"},{"id":105979637,"identity":"5ede69b9-5627-45d7-9fa9-e0e9c8b7188d","added_by":"auto","created_at":"2026-04-02 06:30:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19008,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9291938/v1/36e598175dbd081c190075db.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFrom Knowledge to Intention: Developing and Validating a Scale for K-12 Teachers' Readiness to Teach AI\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial intelligence (AI) is reshaping work, knowledge production, and the skill expectations placed on education systems (Di Battista et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Frey \u0026amp; Osborne, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Schools feel this shift acutely. K-12 systems face growing pressure to prepare students to understand AI's logic, applications, and social implications (Bessen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Martinez, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Panth \u0026amp; Maclean, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 AI Education at the K-12 Level\u003c/h2\u003e \u003cp\u003eSince 2019, K-12 AI education has shifted from a peripheral topic to an emerging curriculum field, supported by frameworks that specify what students should know about AI and how those ideas can be taught in age-appropriate ways (Kim et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Long \u0026amp; Magerko, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Touretzky et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Reviews of this literature converge on three themes. First, K-12 AI education is expected to address foundational concepts, machine learning, and the social consequences of AI rather than narrow technical training alone (Casal-Otero et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rizvi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Second, instruction tends to rely on applied, student-centered approaches such as project-based learning and collaborative design tasks (Ng et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sanusi et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Third, and most critically for implementation, teacher preparedness remains a persistent bottleneck: teachers need domain knowledge, pedagogical strategies, and confidence to teach AI, yet these capacities are unevenly developed and rarely assessed through robust instruments (Kim et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Ng et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yue et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These global patterns are evident in Turkey, where the broader policy movement identified by UNESCO (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) as dependent on both curriculum design and teacher capacity is already underway. The National Artificial Intelligence Strategy 2021\u0026ndash;2025 (T.C. Cumhurbaşkanlığı Dijital D\u0026ouml;n\u0026uuml;ş\u0026uuml;m Ofisi [CBDDO], 2021) and the AI Applications curriculum issued by the Ministry of National Education (T.C. Mill\u0026icirc; Eğitim Bakanlığı [MEB], 2023) indicate that AI teaching is no longer a speculative future concern, but an implementation challenge for schools and teachers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 The Critical Role of Teachers\u003c/h2\u003e \u003cp\u003eTeachers are central to whether AI curricula become meaningful classroom practice. In many school systems, and particularly in the Turkish context, information technology (IT) teachers are among the most likely implementers of formal AI instruction. Their preparedness therefore has both a competence dimension, concerning whether they believe they have the knowledge and pedagogical capacity to teach AI, and an intention dimension, concerning whether they are willing and motivated to do so.\u003c/p\u003e \u003cp\u003eExisting studies point to this dual challenge, but they do so unevenly. Research has examined teachers' motivation and behavioral intention toward AI teaching (Chai et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ayanwale et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sanusi et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), their AI-related knowledge or TPACK readiness (Kim et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Yue et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and instruments for adjacent constructs such as using AI as a pedagogical tool, general AI self-efficacy, or teacher acceptance of AI (Celik, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ramazanoğlu \u0026amp; Akın, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang \u0026amp; Chuang, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, these strands remain largely separate, and none addresses readiness to teach AI as curriculum content with an integrated measurement approach. Cross-national evidence reinforces this gap: Du et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that even teachers with moderate AI literacy felt unprepared to adapt AI curricula to their classrooms, and Addo (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported similar barriers among UK teachers \u0026mdash; highlighting the disconnect between general awareness and teaching-specific readiness. To our knowledge, no validated instrument jointly measures knowledge-based readiness and behavioral intention for teaching AI as a school subject \u0026mdash; a gap especially acute in Turkey, where policy attention to AI education has advanced more quickly than validated assessment tools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Purpose of the Study\u003c/h2\u003e \u003cp\u003eAccordingly, this study aimed to develop and validate a psychometrically sound instrument, the Readiness to Teach AI and Behavioral Intention Scale (RTAI-BIS), for measuring IT teachers' readiness to teach AI and their behavioral intentions toward doing so.\u003c/p\u003e \u003cp\u003eThe study was guided by the following research questions:\u003c/p\u003e \u003cp\u003e1. What is the factor structure of the RTAI-BIS as determined by exploratory factor analysis? 2. To what extent does confirmatory factor analysis support the factor structure identified through EFA? 3. Does the RTAI-BIS demonstrate acceptable levels of reliability?\u003c/p\u003e \u003cp\u003eThe RTAI-BIS is designed to support readiness profiling of teacher populations, evaluation of professional development interventions, and cross-regional comparison of teacher preparedness for AI instruction.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Theoretical Framework","content":"\u003cp\u003eThe RTAI-BIS was informed by two complementary frameworks: the Technological Pedagogical Content Knowledge (TPACK) framework and the Theory of Planned Behavior (TPB). Together, they provide a basis for conceptualizing teacher preparedness as both a competence issue and an intention issue.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Technological Pedagogical Content Knowledge (TPACK)\u003c/h2\u003e \u003cp\u003eThe TPACK framework (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) extends pedagogical content knowledge by emphasizing that effective teaching with technology requires the coordinated use of content knowledge, pedagogical knowledge, and technological knowledge (Koehler \u0026amp; Mishra, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mishra \u0026amp; Koehler, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Shulman, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1986\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). In the context of AI education, this means that teachers need not only to understand AI concepts, but also to select suitable pedagogical approaches and use technological tools in ways that make those concepts teachable to K-12 learners.\u003c/p\u003e \u003cp\u003eTPACK has become a common lens for examining teacher readiness in technology-rich settings (Koehler \u0026amp; Mishra, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). In AI education specifically, Kim et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) argued that TPACK can frame the knowledge teachers need for K-12 AI instruction, and Yue et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that teachers' AI-related content and technological knowledge may lag behind their general pedagogical confidence. At the same time, newer AI-focused extensions such as Intelligent-TPACK and AI-TPACK often target teachers' capacity to use AI as a teaching tool rather than to teach AI as curricular content (Celik, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The RTAI-BIS adopts TPACK in this latter, subject-teaching sense: the readiness component of the RTAI-BIS was designed to capture teachers' perceived readiness to teach AI concepts, practices, and applications.\u003c/p\u003e \u003cp\u003eWithin the TPACK model, technological knowledge (TK) is one component of the broader TPACK framework rather than a stand-alone representation of integrated instructional knowledge (Mishra \u0026amp; Koehler, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, in emerging subject areas such as AI education \u0026mdash; where the tools themselves are the curricular content \u0026mdash; TK-related competencies may be empirically separable from the broader pedagogical-content integration that TPACK represents. Whether this separation manifests in the present context is treated as an empirical question.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Theory of Planned Behavior (TPB)\u003c/h2\u003e \u003cp\u003eThe TPB explains behavior through the mediating role of intention, which is shaped by attitude toward the behavior, subjective norms, and perceived behavioral control (Ajzen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1991\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Applied to AI teaching, these components concern whether teachers see teaching AI as worthwhile, perceive social support or pressure around it, and believe they have sufficient capability and resources to do it.\u003c/p\u003e \u003cp\u003eThis framework is relevant because intention-based models consistently explain teachers' adoption of new technologies and practices (Ajzen, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Davis, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Scherer et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). TPB has been applied to domain-specific teaching intentions \u0026mdash; for example, Lin and Williams (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) modeled preservice teachers' STEM teaching intentions and found perceived behavioral control and subjective norms to be the strongest predictors. In AI education specifically, prior work has linked intention to attitudes, efficacy beliefs, confidence, and perceived relevance (Ayanwale et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chai et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sanusi et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accordingly, the behavioral-intention component of the RTAI-BIS was designed with TPB as its conceptual basis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Integrating TPACK and TPB: The Conceptual Foundation of the RTAI-BIS\u003c/h2\u003e \u003cp\u003eTPACK and TPB were used together because readiness to teach AI cannot be understood only as knowledge possession or only as willingness to act. TPACK helps specify what teachers need to know to teach AI, whereas TPB helps explain whether they are disposed to do so within their institutional context. A similar dual-framework strategy has been employed by Habibi et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who integrated TPB with TPACK to model preservice teachers' technology integration intentions and found that the combination explained substantially more variance than either framework alone. The initial item pool was therefore constructed to represent both readiness-related and TPB-informed intention-related dimensions (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for the theoretical-to-empirical mapping). How the TPB-informed items performed during scale refinement is examined in the Discussion (Section \u003cspan refid=\"Sec36\" class=\"InternalRef\"\u003e5.1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eThis study employed a scale development research design to create a valid and reliable instrument for measuring IT teachers' readiness to teach AI and their behavioral intentions. The scale development process followed established guidelines in the literature (B\u0026uuml;y\u0026uuml;k\u0026ouml;zt\u0026uuml;rk, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; DeVellis, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; G\u0026uuml;ng\u0026ouml;r, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Koyuncu \u0026amp; Kılı\u0026ccedil;, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and comprised three major phases: item pool development, validity studies, and reliability studies. The overall scale development procedure is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Item Pool Development\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Literature Review\u003c/h2\u003e \u003cp\u003eIn the initial phase, national and international AI education curricula (both formal and informal), AI textbooks, and scale items used in related empirical studies were systematically reviewed. This review served to identify the content domain and inform the generation of candidate items aligned with the TPACK and TPB frameworks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Item Generation\u003c/h2\u003e \u003cp\u003eBased on the literature review, a preliminary pool of 294 items was generated. The researchers refined this pool by eliminating overlapping or redundant items while ensuring alignment with the theoretical foundations, yielding a draft pool of 52 items for expert review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Expert Review\u003c/h2\u003e \u003cp\u003eThe 52-item draft was submitted to a panel of six academic experts for content validity evaluation, linguistic appropriateness assessment, and face validity review. Expert panel details are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eExpert Panel Composition\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpert\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty / Department\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Education, Computer Education and Instructional Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfessor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Education, Computer Education and Instructional Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssociate Professor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Education, Computer Education and Instructional Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResearch Assistant (PhD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Education, Educational Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfessor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Education, Educational Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssociate Professor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty of Education, Foreign Languages Education (Language Specialist)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssistant Professor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFollowing expert review, seven items were removed and modifications were applied to selected items based on expert recommendations, resulting in a 45-item candidate scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Pilot Testing\u003c/h2\u003e \u003cp\u003eA pilot study was conducted to further assess face validity. The 45-item candidate scale was converted into a Google Forms questionnaire and administered online to 40 senior preservice IT teachers enrolled at Yıldız Technical University, Department of Computer Education and Instructional Technology. Of the 40 participants, 10 provided detailed and usable written feedback on item clarity and relevance. Based on this feedback, final adjustments were made to the candidate scale, which retained its 45-item structure. Although the pilot sample comprised preservice teachers rather than practicing IT teachers, the pilot's purpose was limited to face validity and item clarity assessment, for which this population was considered appropriate.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Participants\u003c/h2\u003e \u003cp\u003eTwo independent samples of IT teachers serving in Turkish public and private schools were recruited using non-probability snowball sampling (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Because no centralized registry of IT teachers exists in Turkey, snowball sampling was selected to maximize geographic reach within this dispersed population. Participants responded to the online questionnaire distributed via electronic channels. Across both study phases combined, respondents represented 77 of Turkey's 81 provinces (71 in the EFA sample, 58 in the CFA sample), indicating that the sampling chains extended well beyond localized networks.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy 1 (Exploratory Factor Analysis)\u003c/strong\u003e \u003cp\u003eA total of 392 IT teachers participated in the EFA phase.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy 2 (Confirmatory Factor Analysis)\u003c/strong\u003e \u003cp\u003eA total of 258 IT teachers participated in the CFA phase. This sample exceeds the minimum of 200 cases recommended for ML-based CFA (Kline, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and provides approximately four observations per freely estimated parameter in the final model (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eParticipant Demographics by Study Phase\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy 1 \u0026mdash; EFA (n\u0026thinsp;=\u0026thinsp;392)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStudy 2 \u0026mdash; CFA (n\u0026thinsp;=\u0026thinsp;258)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior AI Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior AI Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote.\u003c/em\u003e The two samples were independently recruited via snowball sampling and did not overlap. The demographic distributions were comparable across the two phases, supporting the suitability of independent-sample cross-validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Data Collection Instrument\u003c/h2\u003e \u003cp\u003eThe candidate scale comprised 45 items. Most items were rated on a 5-point agreement scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 2\u0026thinsp;=\u0026thinsp;Disagree, 3\u0026thinsp;=\u0026thinsp;Undecided, 4\u0026thinsp;=\u0026thinsp;Agree, 5\u0026thinsp;=\u0026thinsp;Strongly Agree). However, the five AI Interest and Engagement (AIE) items (M41\u0026ndash;M45) used a 5-point frequency format (1\u0026thinsp;=\u0026thinsp;Never, 2\u0026thinsp;=\u0026thinsp;Rarely, 3\u0026thinsp;=\u0026thinsp;Sometimes, 4\u0026thinsp;=\u0026thinsp;Often, 5\u0026thinsp;=\u0026thinsp;Very Often), because they were intended to capture behavioral engagement with AI rather than attitudinal endorsement. This mixed response-format design was retained in both study administrations and is considered in interpreting the CFA results (see Section \u003cspan refid=\"Sec28\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eItems were designed to assess two overarching dimensions: (a) readiness to teach AI, informed by the TPACK framework, and (b) behavioral intentions related to teaching AI, informed by the TPB. Within the TPB-informed dimension, the initial item pool included items operationalizing all three TPB components. These were attitude toward the behavior (e.g., beliefs about the value of AI education for students), subjective norms (e.g., perceived expectations of colleagues and administrators regarding teaching AI), and perceived behavioral control (e.g., self-efficacy beliefs and perceived resource access for AI instruction). The instrument also included a personal information form collecting demographic data.\u003c/p\u003e \u003cp\u003eThe scale items were originally developed in Turkish. For the purposes of international dissemination, a forward-backward translation procedure aligned with the ITC Guidelines for Translating and Adapting Tests (International Test Commission, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was conducted to produce an English version of the scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Data Analysis\u003c/h2\u003e \u003cp\u003eThe study used IBM SPSS v25 for exploratory factor analysis and IBM SPSS AMOS v26 for confirmatory factor analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCommon Method Bias.\u003c/b\u003e Because the study relied on a single self-report instrument administered at one time point, common method bias (CMB) is a potential concern (Podsakoff et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Rather than relying on Harman's single-factor test, which has been shown to be an insensitive diagnostic (Podsakoff et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), CMB risk was evaluated through converging structural evidence. Specifically, the multi-factor CFA solution, HTMT values below .90, and AVE values above recommended thresholds were collectively examined to assess whether a single-method factor could plausibly account for the observed correlations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExploratory Factor Analysis (EFA).\u003c/b\u003e Prior to factor extraction, the analysis examined item-total correlations to identify items with insufficient discrimination. The Kaiser-Meyer-Olkin (KMO) measure and Bartlett's Test of Sphericity assessed sampling adequacy. The study employed principal component analysis for factor extraction. Principal component analysis was selected for data reduction at the exploratory stage. With communalities consistently above .50 and the majority above .70, PCA and common factor methods such as principal axis factoring are expected to yield convergent solutions (Costello \u0026amp; Osborne, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fabrigar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The number of factors was determined based on the Kaiser criterion (eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1) and scree plot analysis. Direct Oblimin rotation was applied given the theoretical expectation of correlated factors. Items were retained based on factor loading thresholds (\u0026ge;\u0026thinsp;.40) and the absence of cross-loadings exceeding .10 difference between factors (Koyuncu \u0026amp; Kılı\u0026ccedil;, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yaşlıoğlu, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To verify that the factor structure was not contingent on the extraction method, the analysis was replicated using Principal Axis Factoring (PAF); results are reported in Section \u003cspan refid=\"Sec27\" class=\"InternalRef\"\u003e4.1.5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConfirmatory Factor Analysis (CFA).\u003c/b\u003e The factor structure identified through EFA was tested using CFA with the independent sample. CFA was conducted using Maximum Likelihood (ML) estimation. Prior to analysis, univariate and multivariate normality were examined. Univariate skewness values ranged from \u0026minus;\u0026thinsp;1.97 to \u0026minus;\u0026thinsp;0.42 and kurtosis values from \u0026minus;\u0026thinsp;0.75 to 3.23, all within the thresholds recommended by Kline (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; |skewness| \u0026lt; 2, |kurtosis| \u0026lt; 7). Although Mardia's multivariate kurtosis was elevated (normalized CR\u0026thinsp;=\u0026thinsp;55.11), simulation research has demonstrated that ML estimation yields robust parameter estimates with 5-category ordinal indicators and sample sizes exceeding 200 (Rhemtulla et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Beauducel \u0026amp; Herzberg, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). To account for potential chi-square inflation, Bollen-Stine bootstrap (500 resamples) was applied (Bollen \u0026amp; Stine, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). As a further robustness check, the CFA model was re-estimated on a polychoric covariance matrix (Tucker's congruence \u0026gt; .99, confirming ML robustness to ordinal measurement).\u003c/p\u003e \u003cp\u003eThe study evaluated model fit using multiple indices: RMSEA, SRMR, GFI, NFI, TLI, CFI, IFI, and χ\u0026sup2;/df (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Kline, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A .70 threshold for standardized regression weights (SRW) was adopted as the primary retention criterion, corresponding to approximately 50% shared variance between an item and its factor (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This threshold is also consistent with the AVE \u0026ge; .50 convergent-validity requirement (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Re-specification followed a sequential, one-item-at-a-time protocol: after each model estimation, the item with the lowest SRW was identified and evaluated for removal (Koyuncu \u0026amp; Kılı\u0026ccedil;, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kline, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Removal decisions were guided primarily by SRW magnitude but also informed by construct alignment and parsimony; items that weakened the definitional focus of their target factor were removed even when their loading was near the threshold. When successive removal left a factor without a stable set of adequately performing indicators, that factor was not retained in the final model. Convergent validity was assessed through Average Variance Extracted (AVE) values, with .50 as the minimum threshold (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Yaşlıoğlu, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Discriminant validity was evaluated using the Heterotrait-Monotrait (HTMT) ratio of correlations, with values below .90 indicating adequate discriminant validity (Henseler et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Configural invariance was examined by fitting the CFA model separately to gender and school type subgroups.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReliability Analysis.\u003c/b\u003e The study assessed internal consistency using Cronbach's alpha (α) for both the overall scale and individual subscales, with .70 as the minimum acceptable threshold (G\u0026uuml;ng\u0026ouml;r, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nunnally \u0026amp; Bernstein, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Composite Reliability (CR) was computed as CR = (Σλ)\u0026sup2; / [(Σλ)\u0026sup2; + Σ(1\u0026thinsp;\u0026minus;\u0026thinsp;λ\u0026sup2;)], where λ denotes standardized factor loadings (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), with .70 as the minimum threshold (Yaşlıoğlu, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). McDonald's omega (ω) was additionally computed as a model-based reliability coefficient that does not assume tau-equivalence (Revelle \u0026amp; Zinbarg, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Item-total correlations and inter-subscale correlations were also examined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Ethical Considerations\u003c/h2\u003e \u003cp\u003eThis study received ethical approval from the Yıldız Technical University Social and Human Sciences Research Ethics Committee (Report No: 20240402851, Decision No: 2024.04; date: April 1, 2024). All participants were informed about the purpose of the study, the voluntary nature of participation, and the confidentiality of their responses. Informed consent was obtained electronically prior to questionnaire completion. No personally identifiable information was collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Data Availability\u003c/h2\u003e \u003cp\u003eThe anonymized dataset generated during this study is available from the corresponding author upon reasonable request.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Exploratory Factor Analysis\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Preliminary Analyses\u003c/h2\u003e \u003cp\u003eItem-total correlations were computed to assess each item's consistency with the overall scale. Four items (M7, M31, M32, M5) demonstrated very low correlation values and were removed from further analyses, leaving 41 items. The Cronbach's alpha coefficient for the remaining 41 items was .976 (alpha was not computed at the initial 45-item stage, as it would have been deflated by items with near-zero correlations).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCommon Method Bias Assessment.\u003c/b\u003e Because all data were collected through a single self-report instrument, CMB was evaluated through converging structural evidence (Podsakoff et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e): (a) the EFA distributed variance across five factors rather than one, (b) all HTMT values remained below .90 (see Section \u003cspan refid=\"Sec28\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e), and (c) AVE values exceeded .50 for all factors. These results suggest that common method variance does not provide a plausible single-factor explanation for the observed correlations; procedural remedies for future administrations are discussed in Section \u003cspan refid=\"Sec40\" class=\"InternalRef\"\u003e5.5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe Kaiser-Meyer-Olkin (KMO) test and Bartlett's Test of Sphericity were conducted to evaluate the suitability of the data for factor analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eKMO and Bartlett's Test of Sphericity Results\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMO Measure of Sampling Adequacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett's Test of Sphericity \u0026mdash; Approx. χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17437.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett's Test of Sphericity \u0026mdash; df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett's Test of Sphericity \u0026mdash; p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;392.\u003c/p\u003e \u003cp\u003eBoth indices confirmed that the data were suitable for factor analysis. The KMO value (.966) well exceeded the recommended .60 threshold, and Bartlett's test was statistically significant (B\u0026uuml;y\u0026uuml;k\u0026ouml;zt\u0026uuml;rk, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Koyuncu \u0026amp; Kılı\u0026ccedil;, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Factor Extraction\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis was conducted with the remaining 41 items. The Kaiser criterion identified five factors with eigenvalues greater than 1.0 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The scree plot analysis corroborated this five-factor solution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEigenvalues and Explained Variance for the First 10 Components\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe scree plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) visually confirmed the five-factor solution, with a clear inflection point after the fifth component.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCommunality values for all 41 items were above .50, indicating that the extracted factors adequately accounted for the variance in each item (Erkuş, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yaşlıoğlu, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Factor Rotation and Item Retention\u003c/h2\u003e \u003cp\u003eThe unrotated component matrix was examined, and four items (M6, M8, M13, M15) that did not meet the retention criteria (factor loading \u0026ge; .40 on any factor, or cross-loading difference \u0026lt; .10) were removed.\u003c/p\u003e \u003cp\u003eDirect Oblimin oblique rotation was applied to the remaining 37 items, based on the theoretical expectation of inter-factor correlations. The rotated component matrix is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRotated Component Matrix (Direct Oblimin) \u0026mdash; Factor Loadings\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFactor 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote.\u003c/em\u003e Only primary factor loadings are displayed. Factor loadings \u0026lt; .40 are suppressed for clarity. Negative loadings on Factor 5 reflect the oblique rotation direction and do not indicate reverse-scored items.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInter-Factor Correlation Matrix\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe inter-factor correlations (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) confirmed the appropriateness of the oblique rotation method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 EFA Summary\u003c/h2\u003e \u003cp\u003eThe EFA yielded a five-factor structure with 37 items, explaining 73.79% of the total variance (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Factor labels were assigned based on the theoretical relationships of the items within each factor. This solution was treated as a candidate structure; full reliability analyses were conducted on the CFA-confirmed model (see Section \u003cspan refid=\"Sec31\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEFA Factor Structure Summary\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItem Codes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Variance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnological, Pedagogical, and Content Knowledge for Teaching AI (TPACK-TAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM16, M17, M18, M19, M20, M22, M23, M24, M25, M26, M27, M28, M29, M30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude Toward AI (ATA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM33, M34, M35, M36, M37, M38, M39, M40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI Interest and Engagement (AIE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM41, M42, M43, M44, M45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisposition Toward Teaching AI (DTAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM1, M2, M3, M4, M9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnological Knowledge for Teaching AI (TK-TAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM10, M11, M12, M14, M21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.1.5 Extraction Method Robustness Check\u003c/h2\u003e \u003cp\u003eTo confirm that the factor structure was not an artifact of the extraction method, the EFA was replicated using Principal Axis Factoring (PAF) with Direct Oblimin rotation. The PAF solution recovered the same five-factor structure with identical item-to-factor assignments. Tucker's congruence coefficients between the PCA and PAF pattern matrices ranged from .92 to .99, exceeding the .85 threshold for fair similarity (Lorenzo-Seva \u0026amp; ten Berge, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The PAF solution explained 61.07% of total variance, consistent with the expected reduction when modeling common variance only (Costello \u0026amp; Osborne, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fabrigar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Confirmatory Factor Analysis\u003c/h2\u003e \u003cp\u003eThe five-factor, 37-item structure from EFA was tested with the independent CFA sample (n\u0026thinsp;=\u0026thinsp;258) using IBM SPSS AMOS v26. Item retention followed the sequential re-specification procedure described in Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e3.5\u003c/span\u003e. At each step, the item with the lowest SRW was identified, evaluated for both statistical performance and substantive alignment with the target construct, and removed before re-estimating the model. This process was repeated until all remaining items met the .70 threshold and model fit indices reached acceptable levels. Across successive re-specifications, a total of 11 items were removed in the following order: M4, M44, M45, M41, M42, M43, M39, M9, M34, M16, and M19. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e summarizes the removed items, their original EFA factor assignments, and the removal rationale.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eItems Removed During CFA Re-Specification\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOriginal EFA Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRW at Removal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrimary Removal Basis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDTAI (F4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow loading; less aligned with retained DTAI core\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIE (F3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow loading; personal AI-use behavior; frequency format\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIE (F3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow loading; personal AI-use behavior; frequency format\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIE (F3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow loading; personal AI-use behavior; frequency format\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIE (F3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePersonal AI-use behavior; frequency format; factor collapsing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIE (F3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinal AIE indicator; factor eliminated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATA (F2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eContent overlap with retained ATA items\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDTAI (F4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConditional willingness; less aligned with retained DTAI core\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATA (F2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeripheral to retained ATA definition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTPACK-TAI (F1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBroader AI knowledge; less teaching-specific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTPACK-TAI (F1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBroader societal awareness; less teaching-specific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote.\u003c/em\u003e SRW\u0026thinsp;=\u0026thinsp;standardized regression weight at the model estimation step immediately preceding the item's removal. Items 1\u0026ndash;4 fell below the .70 threshold. Items 5\u0026ndash;11 were removed primarily on conceptual grounds, as detailed below.\u003c/p\u003e \u003cp\u003eThe removals followed three patterns: (a) all five AIE items (M41\u0026ndash;M45) measured personal AI tool-use frequency rather than teaching-specific capability, and their frequency response format introduced heterogeneity (Podsakoff et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), leading to elimination of the AIE factor; (b) M4 and M9 originated from TPB components whose content diverged from the retained DTAI core; and (c) the remaining items (M34, M39, M16, M19) reflected diffuse evaluations or broad knowledge peripheral to the target constructs. Theoretical implications of these patterns are discussed in Section \u003cspan refid=\"Sec36\" class=\"InternalRef\"\u003e5.1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe remaining four factors retained their conceptual coherence and all item SRWs in the final 26-item model exceeded .70 (see Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). Because the AIE factor was eliminated, the surviving factors were renumbered: TK-TAI moved from EFA Factor 5 to CFA Factor 3, and DTAI moved from EFA Factor 4 to CFA Factor 4.\u003c/p\u003e \u003cp\u003eModel fit was further improved by examining modification indices. The largest modification index indicated a covariance between the error terms of items M20 (\"I can explain how AI applications work using examples\") and M22 (\"I can explain the strengths and weaknesses of AI technology\"), both of which loaded on the TPACK-TAI factor and shared overlapping content related to AI content knowledge. This single error covariance was specified based on both statistical and substantive grounds. No further modifications were warranted, as remaining modification indices did not indicate theoretically justifiable re-specifications. The final model (χ\u0026sup2; = 759.910, df\u0026thinsp;=\u0026thinsp;292, p \u0026lt; .001) is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and model fit indices are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCFA Model Fit Indices\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcceptable Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2;(292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e759.910, p \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2;/df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u0026uuml;ng\u0026ouml;r (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.079, 90% CI [.072, .086]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKoyuncu \u0026amp; Kılı\u0026ccedil; (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYaşlıoğlu (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u0026uuml;ng\u0026ouml;r (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKoyuncu \u0026amp; Kılı\u0026ccedil; (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYaşlıoğlu (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u0026uuml;ng\u0026ouml;r (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKoyuncu \u0026amp; Kılı\u0026ccedil; (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll primary model fit indices (CFI, TLI, RMSEA, SRMR) were within acceptable ranges, supporting the construct validity of the four-factor, 26-item structure. The RMSEA point estimate (.079) fell within the adequate-fit range, and its 90% confidence interval [.072, .086] indicated that the upper bound remained below .10, reinforcing the acceptability of the model fit. GFI (.810) fell below .90 but is no longer recommended as a primary fit index (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); model adequacy rests on the primary indices above. Bollen-Stine bootstrap (500 resamples) similarly yielded p \u0026lt; .001, consistent with the known sensitivity of χ\u0026sup2; to sample size.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAverage Variance Extracted (AVE) Values\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 1 \u0026mdash; TPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 2 \u0026mdash; ATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 3 \u0026mdash; TK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 4 \u0026mdash; DTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll AVE values exceeded the .50 threshold, indicating adequate convergent validity (Yaşlıoğlu, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiscriminant validity was further evaluated using the Heterotrait-Monotrait (HTMT) ratio of correlations (Henseler et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). HTMT values are presented in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eHTMT Discriminant Validity Matrix\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll HTMT values were below the conservative .90 threshold, supporting discriminant validity across all factor pairs. The highest HTMT value was observed between TPACK-TAI and TK-TAI (.864), which is theoretically expected given that both factors capture knowledge-based dimensions of the TPACK framework. All remaining HTMT values were below the stricter .85 criterion.\u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 CFA Summary\u003c/h2\u003e \u003cp\u003eThe CFA confirmed a four-factor structure with 26 items. The final factor structure is presented in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eFinal Scale Structure After CFA\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItem Codes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Regression Weights\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnological, Pedagogical, and Content Knowledge for Teaching AI (TPACK-TAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM17, M18, M20, M22, M23, M24, M25, M26, M27, M28, M29, M30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.705\u0026ndash;.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude Toward AI (ATA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM33, M35, M36, M37, M38, M40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.897\u0026ndash;.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnological Knowledge for Teaching AI (TK-TAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM10, M11, M12, M14, M21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.823\u0026ndash;.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisposition Toward Teaching AI (DTAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM1, M2, M3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.911\u0026ndash;.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlthough DTAI retained only three items, this meets the minimum identification requirement for CFA (Kline, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The retained items demonstrated uniformly high SRWs (.911\u0026ndash;.924), strong AVE (.840), and high composite reliability (.930), reflecting a coherent construct core: evaluative beliefs about the importance of AI education for students' futures.\u003c/p\u003e \u003cp\u003eThe retention of both TPACK-TAI and TK-TAI as separate factors indicates that integrated instructional readiness and narrower technology-specific capability represent empirically distinguishable dimensions within this sample. The HTMT value between the two factors (.864) remained below the .90 discriminant validity threshold (see Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e), supporting this distinction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eIndividual Standardized Regression Weights from Confirmatory Factor Analysis\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote.\u003c/em\u003e All standardized regression weights exceed the .70 retention threshold. Values extracted from IBM SPSS AMOS v26 output.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Configural Invariance\u003c/h2\u003e \u003cp\u003eTo assess the structural stability of the four-factor model across demographic subgroups, configural invariance was examined by fitting the CFA model separately to gender and school type groups (Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eConfigural Invariance: Model Fit by Subgroup\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrouping Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;/df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote.\u003c/em\u003e RMSEA 90% confidence intervals and SRMR values are not reported for subgroup models because AMOS does not produce stable CI estimates when models are fitted to small samples (n\u0026thinsp;\u0026lt;\u0026thinsp;200) with complex structures (Kenny et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). SRMR for the full-sample model was .061 (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe four-factor structure demonstrated acceptable fit across both gender groups (CFI \u0026ge; .90, χ\u0026sup2;/df\u0026thinsp;\u0026lt;\u0026thinsp;3.0), supporting configural invariance for gender. Elevated RMSEA values in the subgroups are attributable to reduced sample sizes (Kenny et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For school type, the public school subsample showed acceptable fit; the private school subsample (n\u0026thinsp;=\u0026thinsp;75) showed marginally below-threshold CFI (.898), likely attributable to the small subsample size. Overall, these results provide preliminary evidence that the four-factor structure holds across the examined subgroups. Full metric and scalar invariance testing with larger subsamples is recommended for future research.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Reliability Analysis\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Item-Total Correlations\u003c/h2\u003e \u003cp\u003eCorrected item-total correlations for the 26-item scale ranged from .731 to .920 (Table\u0026nbsp;\u003cspan refid=\"Tab15\" class=\"InternalRef\"\u003e15\u003c/span\u003e), indicating that all items were strong representatives of their respective subscales.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrected Item-Total Correlations\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubscale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubscale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Inter-Subscale and Scale-Total Correlations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab16\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 16\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInter-Subscale and Scale-Total Correlations\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTPACK-TAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScale Total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.940\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote.\u003c/em\u003e All correlations are significant at \u003cem\u003ep\u003c/em\u003e \u0026lt; .01 (two-tailed).\u003c/p\u003e \u003cp\u003eInter-subscale correlations (Table\u0026nbsp;\u003cspan refid=\"Tab16\" class=\"InternalRef\"\u003e16\u003c/span\u003e) ranged from .440 to .818, and subscale-to-total correlations ranged from .690 to .940, confirming that all subscales were significantly and highly correlated with each other and with the overall scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Internal Consistency\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab17\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 17\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCronbach's Alpha, Composite Reliability, and McDonald's Omega Values\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale / Subscale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCronbach's α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComposite Reliability (CR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMcDonald's ω\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 1 \u0026mdash; TPACK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 2 \u0026mdash; ATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 3 \u0026mdash; TK-TAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 4 \u0026mdash; DTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInternal consistency was uniformly strong. All Cronbach's α, CR, and McDonald's ω values (Table\u0026nbsp;\u003cspan refid=\"Tab17\" class=\"InternalRef\"\u003e17\u003c/span\u003e) exceeded the .70 threshold across both the overall scale and all subscales (G\u0026uuml;ng\u0026ouml;r, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Revelle \u0026amp; Zinbarg, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yaşlıoğlu, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). McDonald's omega, which accounts for the congeneric measurement model and does not assume tau-equivalence, confirmed the robustness of these estimates. For TPACK-TAI, the notably high alpha (.974) is accompanied by an inter-item correlation mean of .758 (range = .592\u0026ndash;.895), indicating a cohesive factor rather than item redundancy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study developed and validated the Readiness to Teach AI and Behavioral Intention Scale (RTAI-BIS) for K-12 IT teachers. The discussion focuses on what the final structure means theoretically, how the scale relates to prior work, and where its present evidential limits remain.\u003c/p\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Factor Structure and Theoretical Alignment\u003c/h2\u003e \u003cp\u003eThe final RTAI-BIS comprises 26 items organized into four factors, indicating that readiness to teach AI is best represented as a combination of knowledge-related readiness and intention-related orientation rather than a single disposition. This supports the use of TPACK as a framework for the readiness component, consistent with prior work arguing that AI instruction requires domain-specific integration of content, pedagogy, and technology (Kim et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Yue et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The separation between TPACK-TAI and TK-TAI suggests that teachers distinguish between integrated instructional readiness \u0026mdash; encompassing explanation, lesson design, assessment, and pedagogical adaptation \u0026mdash; and narrower technology-specific capability centered on AI tools, software environments, and coding platforms. This interpretation is consistent with item content: TK-TAI items concern use of specific tools and troubleshooting, whereas TPACK-TAI items emphasize concept explanation, instructional planning, and the coordination of pedagogy with technology. The two factors are theoretically related, as TK is embedded within the broader TPACK framework (Mishra \u0026amp; Koehler, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), yet they are empirically non-redundant (HTMT = .864), indicating that tool-level competence and integrated pedagogical-content readiness remain distinguishable in this context. This finding resonates with Velander et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who reported that Swedish K-12 teachers' AI content knowledge was largely acquired through incidental exposure and often contained misconceptions, underscoring that technological familiarity and pedagogical-content readiness develop along different trajectories.\u003c/p\u003e \u003cp\u003eThe TPB-informed component of the scale is more nuanced. DTAI most closely reflects attitude toward the behavior, because its items concern whether AI education should be provided to students. ATA, by contrast, reflects evaluations of AI as an object or societal phenomenon rather than direct commitment to classroom enactment. The strong ATA-DTAI correlation nevertheless suggests that favorable views of AI and favorable views of teaching AI are empirically connected, even when they are not identical constructs. Habibi et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported a similar pattern: attitudinal beliefs were stronger predictors of preservice teachers' technology integration intentions than knowledge variables alone. In a complementary finding, Addo and Sentance (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used self-determination theory to explore K-12 teachers' motivation for teaching AI and found that intrinsic interest in AI, perceived competence, and institutional support were key motivational drivers. These drivers correspond to the ATA and DTAI dimensions of the RTAI-BIS, reinforcing the view that dispositional and evaluative orientations play a central role in shaping teaching behavior.\u003c/p\u003e \u003cp\u003eA central interpretive issue concerns why subjective norms and perceived behavioral control did not survive as independent factors. Subjective norm variance clustered with the dispositional teaching factor, while perceived behavioral control items overlapped with knowledge-related factors \u0026mdash; consistent with evidence that normative and control beliefs often merge with attitudinal and capability constructs in teaching contexts (Ajzen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Armitage \u0026amp; Conner, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bandura, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Cheung \u0026amp; Cheung Tse, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This indicates that some TPB components are latently embedded in broader readiness and disposition constructs rather than manifesting as stand-alone subscales.\u003c/p\u003e \u003cp\u003eThe shift from five EFA factors to four CFA factors reinforces this interpretation. The removed AI Interest and Engagement factor captured personal engagement with AI tools \u0026mdash; behaviors that reflect how often teachers use AI in their own lives rather than whether they are prepared to teach it. Because such personal-use behaviors are likely too transient and idiosyncratic to constitute a stable component of teaching readiness, the factor did not replicate under confirmatory conditions. A response-format difference may also have contributed, as the AIE items used a frequency scale rather than the Likert agreement format of all other subscales (Podsakoff et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The retained four-factor model is more tightly aligned with the study's construct focus: teaching-related capability, technology-related capability, attitudinal orientation, and dispositional commitment. The scale therefore captures not general AI enthusiasm but readiness and willingness to teach it.\u003c/p\u003e \u003cp\u003eThe transition from five EFA factors to four CFA factors reflects cross-sample refinement rather than fit maximization. The .70 SRW threshold was set a priori, and each removal was evaluated for conceptual as well as statistical justification (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Crucially, the AIE dimension was proposed by the EFA sample and independently tested by the CFA sample; its failure to replicate is a genuine cross-validation outcome. That the surviving model produced uniformly high AVE values (.700\u0026ndash;.840), clean discriminant validity (all HTMT \u0026lt; .90), and configural invariance across subgroups provides converging evidence against post-hoc overfitting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Psychometric Properties\u003c/h2\u003e \u003cp\u003eThe psychometric results indicate that the final instrument is internally coherent and structurally consistent across independent samples. The EFA and CFA results converged on a structure with interpretable factors, acceptable model fit, and strong item-factor relations. Convergent validity was supported by AVE values above recommended thresholds, and discriminant validity was supported by HTMT values below the conservative .90 criterion (Henseler et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The highest HTMT value occurred between TPACK-TAI and TK-TAI, which is theoretically expected because both factors concern knowledge-related readiness; importantly, the value still remained below the threshold, suggesting related but distinguishable constructs.\u003c/p\u003e \u003cp\u003eReliability evidence was similarly strong. Cronbach's alpha, composite reliability, and McDonald's omega all supported high internal consistency for the total scale and subscales, while item-total correlations indicated that the retained items contributed meaningfully to their respective dimensions. The notably high alpha of TPACK-TAI (.974) warrants comment: with 12 items and corrected item-total correlations ranging from .731 to .920, this value reflects a cohesive factor rather than item redundancy, yet it suggests that a shorter form may be feasible without substantial information loss. Future studies should evaluate whether a reduced TPACK-TAI subscale can maintain comparable validity and reliability. Configural invariance results also suggest that the four-factor model is structurally plausible across gender and school type groups, although stronger subgroup evidence is still needed before broader invariance claims are made.\u003c/p\u003e \u003cp\u003eGiven these results, the RTAI-BIS is suitable for research and diagnostic use at the current stage of validation. The retained factor structure appears sufficiently coherent to support substantive interpretation rather than only exploratory description.\u003c/p\u003e \u003cp\u003eAlthough a dedicated criterion-validation design was not part of the present study, the broader dataset provides limited external-pattern evidence. Teachers who had completed prior AI training scored significantly higher on TPACK-TAI (t(256)\u0026thinsp;=\u0026thinsp;4.32, p \u0026lt; .001, d\u0026thinsp;=\u0026thinsp;0.54) and TK-TAI (t(256)\u0026thinsp;=\u0026thinsp;5.11, p \u0026lt; .001, d\u0026thinsp;=\u0026thinsp;0.64), with the stronger differentiation on TK-TAI consistent with the expectation that technological knowledge is more directly shaped by formal training. These patterns constitute preliminary known-groups evidence but do not substitute for a formal criterion-validation study based on external performance measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Comparison with Existing Literature\u003c/h2\u003e \u003cp\u003eAs noted in Section \u003cspan refid=\"Sec1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, prior work has approached readiness and intention separately. The RTAI-BIS addresses this gap by integrating TPACK-based competence assessment with TPB-informed intention measurement in a single validated instrument (cf. Ayanwale et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chai et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Yue et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe distinction from adjacent AI-related instruments is equally important. Several recent scales measure teachers' competence in using AI as a pedagogical tool, or their general AI self-efficacy, rather than their readiness to teach AI as curricular content (Celik, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chiu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang \u0026amp; Chuang, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The closest comparable instrument, the TAAI (Guo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), assesses teachers' acceptance of AI in education through TAM-based dimensions including perceived usefulness, ease of use, self-efficacy, and anxiety; however, acceptance of AI as an educational resource is conceptually distinct from readiness and intention to teach AI as a subject, which additionally requires pedagogical-content integration and dispositional commitment to classroom enactment. The present scale accordingly addresses this more specific implementation problem: whether teachers are prepared and inclined to teach AI itself. This specificity is important given cross-national evidence that even teachers with moderate AI awareness report feeling unprepared to adapt AI curricula to classroom contexts (Du et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the Turkish context, this is particularly relevant because policy commitments to AI education already exist, but validated tools for assessing teacher preparedness have been lacking (T.C. CBDDO, 2021; T.C. MEB, 2023).\u003c/p\u003e \u003cp\u003eCross-national studies support the multidimensional nature of the construct. Yau et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified six qualitatively distinct categories in how teachers conceptualize teaching AI, aligning with the RTAI-BIS's separation of knowledge-related and intention-related dimensions. Jatileni et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that Namibian teachers' intention to teach AI depended on attitude and confidence rather than on readiness alone, while Ayanwale and Sanusi (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported significant STEM vs. non-STEM group differences that underscore the importance of domain-specific measurement. Within Turkey, although intention-based scale research has effectively captured teaching readiness for emerging domains (G\u0026uuml;nbatar \u0026amp; Bakırcı, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), no analogous instrument existed for AI \u0026mdash; a gap the RTAI-BIS now addresses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Practical Implications\u003c/h2\u003e \u003cp\u003ePractically, the RTAI-BIS can function as a diagnostic tool for curriculum implementation. School leaders and teacher educators can use it to identify whether weak readiness stems more from pedagogical-content capability, technology-related capability, or attitudinal-dispositional barriers. Consider two contrasting profiles: a teacher scoring high on TPACK-TAI and TK-TAI but low on DTAI has the knowledge to teach AI yet lacks the dispositional commitment to do so \u0026mdash; an implementation gap that training alone cannot close. Conversely, a teacher scoring high on DTAI and ATA but low on TPACK-TAI is willing and favorably disposed but lacks the pedagogical-content readiness \u0026mdash; a training gap amenable to structured professional development. Teacher education providers can use such profiles to design targeted preservice and in-service support rather than treating AI preparation as a single undifferentiated training need.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eNo single validation study is fully definitive. Several limitations should be considered. The study was conducted only with IT teachers in Turkey, so the findings cannot yet be generalized across countries, school systems, or teacher populations. Sampling also introduces a concern: non-probability snowball sampling may have introduced selection bias. Relatedly, the instrument relies on self-report data and thus cannot be assumed to reflect actual classroom practice. As reported in Section \u003cspan refid=\"Sec23\" class=\"InternalRef\"\u003e4.1.1\u003c/span\u003e, converging structural evidence mitigated common method bias concerns; however, future studies should employ procedural remedies such as temporal separation or multi-source data collection (Podsakoff et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTemporal stability was not assessed; future studies should estimate test-retest reliability within a 2\u0026ndash;4 week window. The transition from five EFA factors to four CFA factors is both a limitation and a finding: the initial item pool's coverage of personal AI engagement did not survive cross-sample validation, likely because personal tool-use behaviors occupy a different construct space from teaching readiness and because the frequency-scaled items introduced response-format heterogeneity. Future iterations might re-approach the engagement dimension with items anchored to teaching contexts and a consistent response format.\u003c/p\u003e \u003cp\u003eInvariance evidence is preliminary because subgroup sizes, especially for private-school teachers, were limited. On the theoretical side, although the initial pool represented all three TPB components, subjective norms and perceived behavioral control did not remain as independent subscales, limiting the instrument's ability to test the full TPB pathway directly. Finally, the study did not include a dedicated criterion-validation design. The preliminary known-groups evidence reported in Section \u003cspan refid=\"Sec37\" class=\"InternalRef\"\u003e5.2\u003c/span\u003e should not be treated as a substitute for criterion-related validation based on classroom observation, lesson quality, or prospective implementation outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study produced the RTAI-BIS, a four-factor, 26-item instrument that jointly captures TPACK-based readiness and TPB-informed behavioral intention for teaching AI at the K-12 level. The cross-validated factor structure and strong reliability evidence position the scale as a potentially useful diagnostic tool whose multidimensional profile can distinguish knowledge gaps from attitudinal barriers, informing more targeted approaches to teacher preparation rather than uniform training (see Section \u003cspan refid=\"Sec39\" class=\"InternalRef\"\u003e5.4\u003c/span\u003e for profiling applications). The availability of Turkish and English versions facilitates future cross-cultural validation and comparison studies as AI education expands internationally.\u003c/p\u003e \u003cp\u003eKnowledge dimensions (TPACK-TAI, TK-TAI) account for the largest share of explained variance, yet attitudinal and dispositional factors remain empirically distinct and necessary \u0026mdash; suggesting that teacher readiness for AI education may be knowledge-dominant but not knowledge-sufficient, a pattern that warrants confirmation in future samples.\u003c/p\u003e \u003cp\u003eThe findings should be interpreted in light of certain limitations, including the single-country sample, reliance on self-report data, and the absence of test-retest evidence. Several directions deserve priority. Criterion-related validity studies should examine whether RTAI-BIS scores predict observable teaching behaviors and implementation quality. Longitudinal administrations can establish score stability and track how readiness shifts after professional development. Replication across teacher populations outside Turkey and beyond IT teachers will clarify the instrument's generalizability.\u003c/p\u003e \u003cp\u003eAs AI education moves from curriculum documents to classroom practice, the gap between intended instruction and teacher capacity will shape what students actually learn. The RTAI-BIS offers one empirically grounded way to measure that gap.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u003c/strong\u003e This study received ethical approval from the Yıldız Technical University Social and Human Sciences Research Ethics Committee (Report No: 20240402851, Decision No: 2024.04; date: April 1, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e Informed consent was obtained electronically from all participants prior to questionnaire completion. No personally identifiable information was collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The anonymized dataset generated during this study is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Hasan Tokatlı: Conceptualization, methodology, data collection, formal analysis, and writing \u0026ndash; original draft preparation. M. Fatih Erko\u0026ccedil;: Supervision, methodological guidance, contribution to data collection, and review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAddo A (2023) Are you ready to teach AI in schools? Teachers' perspectives of teaching AI in K-12 settings. In Proceedings of the 2023 United Kingdom and Ireland Computing Education Research Conference (pp. 1\u0026ndash;7). 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Educ Inform Technol 28(1):1041\u0026ndash;1064. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-022-11161-x\u003c/span\u003e\u003cspan address=\"10.1007/s10639-022-11161-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue M, Jong MSY, Ng DTK (2024) Understanding K-12 teachers' technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Educ Inform Technol 29(13):16951\u0026ndash;16976. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-024-12621-2\u003c/span\u003e\u003cspan address=\"10.1007/s10639-024-12621-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"readiness to teach AI, behavioral intention, scale development, TPACK, Theory of Planned Behavior, K-12 education","lastPublishedDoi":"10.21203/rs.3.rs-9291938/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9291938/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe growing integration of artificial intelligence (AI) into society has increased demand for K-12 AI education, placing information technology (IT) teachers at the forefront of curriculum delivery. However, validated instruments assessing IT teachers' readiness and motivation toward teaching AI remain scarce. This study developed and validated the Readiness to Teach AI and Behavioral Intention Scale (RTAI-BIS), grounded in the Technological Pedagogical Content Knowledge (TPACK) framework and the Theory of Planned Behavior (TPB). An initial pool of 294 items was refined through literature review, expert panel evaluation (n\u0026thinsp;=\u0026thinsp;6), and pilot testing, yielding a 45-item candidate scale. Exploratory Factor Analysis (EFA) with 392 IT teachers in Turkish schools yielded a five-factor structure explaining 73.79% of variance. Confirmatory Factor Analysis (CFA) with an independent sample (n\u0026thinsp;=\u0026thinsp;258) validated a refined four-factor, 26-item model with acceptable fit (Comparative Fit Index [CFI] = .943, root mean square error of approximation [RMSEA] = .079, standardized root mean square residual [SRMR] = .061). The final four factors are Technological, Pedagogical, and Content Knowledge for Teaching AI (TPACK-TAI), Attitude Toward AI (ATA), Technological Knowledge for Teaching AI (TK-TAI), and Disposition Toward Teaching AI (DTAI), with two knowledge factors capturing related but distinguishable aspects of instructional readiness. Reliability analyses demonstrated strong internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;.921\u0026ndash;.974). The RTAI-BIS offers a psychometrically sound tool for assessing IT teachers' readiness to teach AI and their behavioral intentions, with applications in teacher training and curriculum implementation.\u003c/p\u003e","manuscriptTitle":"From Knowledge to Intention: Developing and Validating a Scale for K-12 Teachers' Readiness to Teach AI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 06:30:23","doi":"10.21203/rs.3.rs-9291938/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"518eab70-8101-4684-9dc7-d57d24ec556c","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65545961,"name":"Educational Psychology"},{"id":65545962,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-04-02T06:30:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 06:30:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9291938","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9291938","identity":"rs-9291938","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}