Exploring Artificial Intelligence (AI) Literacy and Situated Academic Writing of Students in a Philippine State University: Inputs to Writing Intervention Program

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While research on AI literacy is expanding globally, studies connecting AI knowledge with situated academic writing in the Philippine context remain limited. This study examines the AI literacy and academic writing profiles of 54 first-year English major students at Romblon State University. A mixed-method design was employed, utilizing survey questionnaires, and semi-structured interviews. Results indicate that students reported high literacy in critical appraisal (M = 3.74, SD = 0.65) and practical application (M = 3.59, SD = 0.71), but only moderate literacy in technological understanding (M = 3.16, SD = 0.68). Inter-rater evaluations of situated academic writing showed overall proficiency (M = 10.31, SD = 1.80, 68.67%), with relational-reflective (M = 11.13, SD = 1.72, 74%) as the highest-performing domain and writing-essentials (M = 9.56, SD = 1.93, 64%) as the lowest. Correlation analyses revealed positive and statistically significant associations between AI literacy and writing domains, with the strongest relationships found between creative-identity and writing-essentials (r = 0.77) and between critical appraisal and practical application (r = 0.84). Qualitative analysis of interview responses further revealed three major themes: gaps in understanding of AI terminology, strong curiosity to learn more about AI, and challenges in using formal academic language. The findings suggest that while students demonstrate reflective and creative writing competence alongside awareness of AI ethics and applications, they continue to face challenges in technical AI concepts and foundational writing skills. The study proposes the WritIntel program, an annual intervention to be implemented for three years, for continuous longitudinal monitoring and long-term development of AI literacy and academic writing proficiency. AI literacy English major students higher education situated academic writing 1. Introduction According to Moreno, Korzeniowski, and Espósito [29], writing is more than a cognitive activity; it is a process of thinking that involves organizing, revising, and refining ideas to produce a written output subject to evaluation. In the same vein, Akkaya & Aydin [1] emphasizes that academic writing is not only about following genre conventions but also about applying specific principles for reporting and textualizing scientific findings. These perspectives explain the complexity of writing as both a cognitive and social endeavor. Recent scholarship has also begun to examine the intersection between artificial intelligence (AI) literacy and writing. For instance, Alzubi [2] found that 278 EFL learners were literate in generative AI tools, which enhanced their English writing skills. Similarly, Hamouma and Menezla [14] concluded that digital literacy significantly improves academic writing performance among EFL students. However, these studies often focus on writing in general and overlook the importance of context-specific or situated writing tasks. Situated academic writing connects writing practices to broader social and disciplinary contexts. Mitchell [28] argues that successful academic writing involves not only individual strategies but also engagement with disciplinary norms and identity-building. Paré [33] likewise highlights that writing helps uphold community values and facilitates negotiation of meaning between writer and reader. These insights suggest that writing instruction must account for the situated and social nature of academic tasks rather than treating writing as an isolated skill. Despite this, gaps remain in understanding how ESL undergraduates navigate these situated processes. Much of the foundational work on writing strategies, such as the studies by Torrance, Thomas, and Robinson [37], was conducted with native English speakers. Their findings identified strategies ranging from detailed planning and brainstorming to minimal drafting and mental outlining. Yet, few investigations have explored how these strategies manifest among ESL learners in context-specific writing scenarios. In the Philippines, interest in AI tools among students is rising. Instructure [16] reported that over 83% of Filipino students use AI for academic purposes, particularly in research and writing. However, the Descartes Institute [11] report revealed that the country ranked 82nd out of 124 in digital skills. Similarly, Oxford Insights [31] placed the Philippines 65th out of 193 in AI readiness, citing gaps in human capital. Ligot [24] further noted that limitations in education, training, and infrastructure continue to hinder the country’s ability to adopt AI technologies effectively. Although studies have examined AI literacy and academic writing separately, little is known about how students integrate AI into their academic writing, particularly in contexts that demand critical thinking and disciplinary awareness. Teachers have observed that while students frequently use AI tools, many struggle to apply them meaningfully in writing tasks. This disconnect shows the need for research exploring the intersection of AI literacy and situated academic writing in actual classroom practice. 1.1 Purpose and Research Questions This information prompted the researchers to pursue a study on how the artificial intelligence literacy of first-year English major students affect their situated academic writing skills at Romblon State University, Philippines. Specifically, the study sought answers to the following questions: 1. What is the level of artificial intelligence literacy of the respondents in terms of: a. technological understanding, b. critical appraisal, and c. practical application 2. What is the perceived level of situated academic writing self-efficacy of the respondents in terms of: a. writing-essentials, b. relational-reflective, and c. creative-identity? 3. What is the level of situated academic writing proficiency of respondents as evaluated by inter-raters in terms of: a. writing-essentials, b. relational-reflective, and c. creative-identity? 4. Is there any significant relationship between the level of AI literacy and perceived level of situated academic writing of the respondents? 5. What are the experiences and challenges of the respondents with low AI literacy, and low perceived levels of situated academic writing? 6. What intervention program can be proposed based on the results of the study? 2. Theoretical Background This study adopts Situated Learning Theory (SLT) as its central framework to examine the development of artificial intelligence (AI) literacy and situated academic writing among first-year English major students. Proposed by Lave and Wenger [23], SLT views learning as a social process that occurs through participation in a community of practice. According to the theory, learners begin at the periphery of the community through Legitimate Peripheral Participation (LPP) and gradually move toward full participation as they acquire knowledge, skills, and confidence. Learning is therefore embedded within authentic activities, social interactions, and cultural contexts, rather than isolated from them. In the context of this study, students’ acquisition of AI literacy and writing proficiency can be understood as participation in overlapping academic and technological communities of practice. Novices, represented by first-year students, initially demonstrate limited knowledge of technical AI concepts and foundational writing skills. Through engagement with peers, instructors, and AI tools, they progressively build competence in technological understanding, critical appraisal, and practical application, alongside writing-essentials, relational-reflective, and creative-identity domains. These practices involve not only individual skill-building but also interaction with artifacts such as AI-assisted drafts, grammar checkers, and structured feedback, which shape students’ learning trajectories. Situated Learning Theory explains the importance of context and participation in fostering both AI literacy and academic writing. It suggests that students’ movement from peripheral to central participation depends on meaningful engagement with authentic writing tasks, guided practice, and collaborative reflection. By situating learning within real academic challenges and AI-supported activities, the framework highlights how students can develop competence that is both practical and socially grounded. 3. Literature Review 3.1. Artificial Intelligence Literacy The term “AI Literacy” was first introduced by Burgsteiner, Kandlhofer, and Steinbauer [ 8 ] to describe the core competencies required to understand fundamental AI concepts. Building on this, Long and Magerko [ 26 ] defined AI literacy as the ability to critically evaluate AI, collaborate with it, and use it responsibly across diverse contexts. Kong, Cheung, and Zhang [ 20 ] further categorized AI literacy into three components: understanding AI fundamentals, applying AI in assessment, and solving real-world problems using AI concepts. Asio [ 3 ] emphasized AI literacy as both comprehension and application of AI’s principles and implications, while Wang, Rau, and Yuan [ 38 ] explained its role in recognizing AI in everyday contexts, using it effectively, and critically assessing AI-generated information within an ethical framework. Together, these perspectives highlight AI literacy as a multidimensional construct that goes beyond technical skills to include evaluative and ethical dimensions. Given the increasing prominence of AI, it is crucial for individuals—particularly college students, who represent the next generation of professionals and leaders—to cultivate AI literacy [ 3 ]. However, empirical studies reveal mixed results. Gokce et al. [ 12 ] reported moderate literacy in critical appraisal (CA) and practical application (PA), but low performance in technological understanding (TU), with variations linked to gender and AI usage. Similarly, Asio [ 3 ] found moderately high levels of literacy among tertiary students, influenced by academic performance, year level, and gender. Laupichler et al. [ 22 ] observed strong CA and PA but weak TU, while Juma [ 19 ] reported that students valued AI yet lacked both conceptual understanding and trust in using it for academic evaluation. Other studies reinforce this variability. Obenza et al. [ 30 ] found high overall literacy among Davao students. Asirit and Hua [ 4 ] revealed that while 79.2% of students were aware of AI, 21.2% had little knowledge, highlighting the need for stronger AI education. Hornberger et al. [ 15 ] and Teng et al. [ 36 ] both showed stronger literacy among STEM students and those with prior AI experience. Similarly, Cubas and Ersdal [ 10 ] identified a persistent gap between students’ tool usage and their theoretical understanding. At the institutional level, Wood et al. [ 40 ] noted that students and faculty demonstrated limited AI knowledge despite showing strong interest in training. In China, Wang et al. [ 39 ] found students exhibited high ethical AI literacy but continued to show conceptual gaps. Collectively, these studies illustrate that AI literacy remains uneven, shaped by background, exposure, and academic discipline, suggesting the need for specific educational interventions. Despite these advances, there remains a lack of consensus on how AI literacy should be systematically developed across higher education curricula in the Philippines. While several studies have examined awareness levels and usage patterns, fewer have investigated how AI literacy interacts with domain-specific competencies, such as situated academic writing. Moreover, most existing research has focused on technologically advanced contexts, leaving developing countries underrepresented. This imbalance asserts the importance of investigating AI literacy in settings like the Philippines, where students demonstrate high interest in AI but face persistent gaps in digital readiness. Understanding these dynamics is crucial for designing programs that not only raise awareness of AI but also empower students to integrate it meaningfully into their academic and professional practices. 3.2. Situated Academic Writing Situated academic writing emphasizes how writing practices are embedded in broader social, cultural, and institutional contexts. Green [ 13 ] highlighted that writing is not only an individual skill but also a socio-cognitive process shaped by interaction with texts, genres, and disciplinary communities. Successful student writers engage deeply with rhetorical conventions and disciplinary expectations, which supports their integration into academic communities. Sala-Bubaré and Castelló [ 34 ] further noted the importance of writing regulation, calling for approaches that consider both emotional and contextual influences in higher education writing. Their study revealed that writing difficulties often stem not just from technical skills but also from students’ struggles to regulate their emotions and manage contextual demands. Such findings emphasize the need for pedagogical strategies that support students’ holistic development as writers. Further, Lillis [ 25 ] introduced the academic literacies perspective, which frames writing as a socially situated and ideologically shaped practice. Rather than reducing writing to technical correctness, this approach emphasizes identity, power, and epistemology in shaping students’ academic texts. By adopting a dialogic pedagogy, educators can foster deeper engagement and critical interaction, positioning students as active participants in knowledge construction. More recent work has expanded on these perspectives by examining the role of collaboration and dialogue in academic writing development. Bouwer and van der Veen [ 6 ] demonstrated that dialogic writing interventions in upper elementary education improved students’ ability to negotiate meaning and revise their texts effectively. These findings suggest that academic writing instruction should integrate dialogic and collaborative activities that mirror authentic academic practices. Finally, collaborative digital writing contexts provide additional insights into situated academic writing. Paba Argote et al. [ 32 ] analyzed how students engaged in digital argumentative writing and found that dialogic regulation such as peer feedback, negotiation, and co-construction was central to their progress. Such studies show that situated writing is dynamic, socially negotiated, and increasingly mediated by digital tools [ 9 ]. Together, this body of work affirms that academic writing must be taught as a situated, interactive practice shaped by discourse communities, collaboration, and evolving technological environments. 4. Methodology This study employed a mixed-methods research design to comprehensively investigate the relationship between Artificial Intelligence (AI) literacy and situated academic writing among first-year English major students at RSU-College of Education. By integrating both quantitative and qualitative approaches, the study sought to generate a deeper and more specific understanding of students’ literacy in AI, their confidence and performance in situated academic writing, and the challenges they encounter in developing these competencies. The design enabled triangulation of results, thereby enhancing both the breadth and depth of interpretation. A total of 54 first-year students enrolled during the Academic Year 2024–2025 participated in the study. The respondents were divided into two blocks, with Block 1 consisting of 25 students and Block 2 consisting of 29 students. This sample size was deemed appropriate for the scope of the study, providing sufficient representation to explore relationships between AI literacy and situated academic writing proficiency. To measure AI literacy, the Scale for the Assessment of Non-Experts’ AI Literacy (SNAIL) developed by Laupichler et al. [ 21 ] was administered. The instrument consists of 31 items rated on a 5-point Likert scale and covers three dimensions: Technological Understanding (Cronbach’s α = .93), Critical Appraisal (α = .91), and Practical Application (α = .85). The overall reliability coefficient of the SNAIL is .92, indicating excellent internal consistency. The instrument required approximately 30 minutes to complete. To assess the students’ perceived level of situated academic writing, the Situated Academic Writing Self-Efficacy Scale (SAWSES) by Mitchell et al. [ 27 ] was used. This scale consists of 16 items divided into three dimensions: Relational-Reflective Writing (α = .88), Creative Identity (α = .88), and Writing Essentials (α = .81). The overall Cronbach’s alpha for the SAWSES is .94, and the instrument could be completed in approximately 15 minutes. To measure actual proficiency in situated academic writing, participants were tasked with writing an essay on multilingualism based on a contextualized scenario and guided questions. Students were instructed to connect their discussion to one of four Sustainable Development Goals (SDGs) provided by the researchers. The outputs were evaluated by three independent raters using a rubric developed by the researchers to ensure systematic assessment and reliability of scores. Quantitative data were analyzed using SPSS Version 25. Descriptive statistics such as weighted mean and standard deviation were calculated to summarize the levels of AI literacy and perceived situated academic writing. Pearson’s Rho Correlation was used to determine the strength and direction of the linear relationship between the two constructs. Prior to interpretation, assumptions for correlation were examined to ensure the appropriateness of the analyses. Where relevant, the findings were further contextualized with the support of effect size interpretation to indicate the practical significance of results. Qualitative data were derived from semi-structured interviews focusing on students with low AI literacy and low perceived levels of situated academic writing, specifically in the domains of Technological Understanding and Writing Essentials. Interview responses were analyzed using Braun and Clarke’s [ 7 ] six-phase thematic analysis approach, which involved familiarization with the data, generating initial codes, identifying patterns, reviewing and refining themes, and reporting the findings. An inductive coding approach was employed, allowing insights to emerge directly from participants’ accounts. Thematic analysis yielded three core themes: (1) Issues on Understanding and Awareness of AI Terminology, (2) Curiosity and Desire to Learn More About AI, and (3) Challenges in Academic Writing and Use of Formal Language. These themes provided contextualized insights into students’ difficulties and aspirations, complementing the quantitative findings and offering a holistic view of the relationship between AI literacy and situated academic writing. 5. Findings This study generated important insights into the levels of AI literacy and situated academic writing among first-year English major students at RSU-College of Education. The results are organized in line with the research questions. 5.1 Level of Artificial Intelligence (AI) literacy The results in Table 1 indicate that students demonstrated a moderate level of technological understanding (M = 3.16, SD = 0.67), with most respondents reporting occasional ability to explain or describe AI concepts. Items such as explainable AI (M = 3.33, SD = 0.78) and the difference between general and narrow AI (M = 3.32, SD = 0.93) showed relatively higher means, while concepts such as artificial neural networks (M = 2.82, SD = 0.87) and big data (M = 2.96, SD = 0.82) were rated lower, suggesting variability in familiarity with technical aspects. In contrast, critical appraisal recorded a high composite mean (M = 3.69, SD = 0.64). Students consistently reported confidence in identifying ethical issues (M = 3.43, SD = 0.92), describing potential risks (M = 3.83, SD = 0.86), and explaining the importance of data privacy (M = 3.89, SD = 0.77). The highest rating was for describing what artificial intelligence is (M = 3.93, SD = 0.82), showing that most respondents could clearly define and reflect on AI-related concerns. Practical application also obtained a high composite mean (M = 3.60, SD = 0.65). Students reported frequent ability to recognize AI in daily life (M = 3.82, SD = 0.87), explain its growing importance (M = 3.80, SD = 0.81), and identify technical applications supported by AI (M = 3.54, SD = 0.95). Lower means were recorded for naming applications related to natural language processing (M = 3.33, SD = 0.75) and critically evaluating implications in daily routines (M = 3.45, SD = 0.81), suggesting some variation in applied experiences. Overall, the results show that while students demonstrated high levels in critical appraisal and practical application, their technological understanding remained moderate, indicating a need for further development in explaining and analyzing core AI concepts. Table 1. Level of Artificial Intelligence Literacy among English major students at Romblon State University TECHNOLOGICAL UNDERSTANDING I can… Mean SD DI describe how machine learning models are trained, validated and tested. 3.06 0.88 M explain how deep learning relates to machine learning. 3.30 0.77 M explain how rule-based systems differ from machine learning systems. 3.20 0.92 M explain how AI applications make decisions. 3.28 0.83 M explain how “reinforcement learning” works on basic level. 3.17 0.77 M explain the difference between general and narrow artificial intelligence 3.32 0.93 M explain how sensors are used by computers to collect data and can be used for AI purposes. 3.02 0.81 M explain what “artificial neural network” means. 2.82 0.87 M explain how machine learning works at general level. 3.07 0.93 M explain the difference between supervised learning and unsupervised learning. 3.24 0.93 M describe the concept of explainable AI. 3.33 0.78 M explain how some AI systems can act and react in their environment. 3.24 0.89 M describe the concept of big data. 2.96 0.82 M evaluate whether media representations of AI go beyond its actual use. 3.26 0.83 M Composite Mean 3.16 0.67 M CRITICAL APPRAISAL I can… Mean SD DI explain why data privacy must be considered when developing and using AI. 3.89 0.77 H explain why data security must be considered when developing and using AI. 3.83 0.77 H identify ethical issues surrounding AI. 3.43 0.92 H describe risks that may arise when using AI systems. 3.83 0.86 H name weaknesses of AI. 3.46 0.91 H describe potential legal problems that may arise when using AI. 3.43 0.92 H critically reflect on the potential impact of AI on individual and society. 3.70 0.82 H describe why humans play an important role in the development of AI. 3.74 0.81 H describe why data play an important role in the development of AI. 3.69 0.82 H describe what artificial intelligence is. 3.93 0.82 H Composite Mean 3.69 0.64 H PRACTICAL APPLICATION I can… Mean SD DI give examples from my daily life where I might be contact with AI. 3.82 0.87 H name examples of technical applications that are supported by AI. 3.54 0.95 H tell if technologies that I use is supported by AI. 3.65 0.81 H assess if the problem in my field can and should be solved by AI. 3.59 0.84 H name applications in which AI assisted natural language processing/understanding is used. 3.33 0.75 H explain why AI has recently become increasingly important. 3.80 0.81 H critically evaluate the implications of AI applications in at least one daily routine. 3.45 0.81 H Composite Mean 3.60 0.65 H Legend: Mean Descriptive Interpretation (DI) 1.00 - 1.80 Very Low (VL) 1.81 - 2.60 Low (L) 2.61 - 3.40 Moderate (M) 3.41 - 4.20 High (H) 4.21 - 5.00 Very High (VH) 5.2 Level of Situated Academic Writing The results in Table 2 indicate that students perceived themselves to have a generally high level of situated academic writing self-efficacy (Grand Mean = 3.77, SD = 0.52). Among the three domains, Relational-Reflective recorded the highest composite mean (M = 3.98, SD = 0.45). Items such as considering the audience when writing (M = 4.50, SD = 0.61) and using feedback for improvement (M = 4.22, SD = 0.66) were rated very high, while other indicators such as creativity in writing (M = 4.07, SD = 0.75) and reflective practices (M = 4.07, SD = 0.70) also received high ratings, suggesting consistent confidence in this domain. Creative-Identity also showed a high composite mean (M = 3.74, SD = 0.56). Students reported being able to adapt their writing to different assignments (M = 3.78, SD = 0.66) and recognize when they had diverged from audience needs (M = 3.91, SD = 0.68). They also expressed confidence in comfortably expressing academic concepts (M = 3.63, SD = 0.71) and balancing feedback with their own judgment (M = 3.76, SD = 0.73). Writing Essentials recorded the lowest composite mean among the three domains, though it was still rated high (M = 3.59, SD = 0.54). Respondents indicated confidence in overcoming writing difficulties (M = 3.91, SD = 0.62), but lower ratings were observed in the ability to use scholarly academic words (M = 3.43, SD = 0.66) and synthesizing multiple sources (M = 3.44, SD = 0.72). These results suggest that while students demonstrate persistence in writing, technical aspects of academic style and integration of sources may remain areas of challenge. Overall, the findings show that students expressed strong confidence in relational-reflective and creative-identity aspects of writing, while writing essentials scored comparatively lower. This pattern indicates that while students feel capable of adapting, reflecting, and developing a personal academic voice, they may still experience difficulty in areas requiring advanced academic language and synthesis of literature. Table 2. Perceived Level of Situated Academic Writing Self-Efficacy of English Major Students WRITING ESSENTIALS Mean SD DI Even when the writing is hard, I can find ways to overcome my writing difficulties. 3.91 0.62 H I can successfully use scholarly academic words and phrases when writing in my courses. 3.43 0.66 H I can combine or synthesize multiple sources I’ve read to create an original product or text. 3.44 0.72 H Composite Mean 3.59 0.54 H RELATIONAL-REFLECTIVE Mean SD DI I can use creativity when writing an academic paper. 4.07 0.75 H When I write, I can think about my audience and write so they clearly understand my meaning. 4.50 0.61 VH When I receive feedback on my writing, no matter how it makes me feel, I can use that feedback to improve my writing in the future. 4.22 0.66 VH When I reflect on what I am writing, I can make my writing better. 4.07 0.70 H I feel I can give my writing a creative spark and still sound professional. 3.67 0.67 H When I read articles about my topic, the connections I feel with the ideas of other authors can inspire me to express my own ideas in writing. 3.70 0.69 H When I look at the overall picture I’ve presented in my writing, I can assess how all the pieces tell the complete story of my topic or argument. 3.80 0.71 H I feel I can develop my own writing voice (ways of speaking in my writing that are uniquely me). 3.80 0.81 H Composite Mean 3.98 0.45 H CREATIVE-IDENTITY Mean SD DI Even with very specific assignment guidelines, I can find ways of writing my assignment to make it original or unique. 3.61 0.79 H I can comfortably express academic concepts, language, and values in my writing assignments. 3.63 0.71 H I can recognize when I’ve wandered away from writing what my audience needs to know and have begun writing about interesting, but unrelated, ideas. 3.91 0.68 H With each new writing assignment, I can adapt my writing to meet the needs of that assignment. 3.78 0.66 H When I seek feedback on my writing, I can decide when that feedback should be ignored or incorporated into a revision in my writing. 3.76 0.73 H Composite Mean 3.74 0.56 H GRAND MEAN 3.77 0.52 H Legend: Mean Descriptive Interpretation (DI) 1.00 - 1.80 Very Low (VL) 1.81 - 2.60 Low (L) 2.61 - 3.40 Moderate (M) 3.41 - 4.20 High (H) 4.21 - 5.00 Very High (VH) The results in Table 3 indicate that students were rated as proficient in all three domains of situated academic writing, with an overall mean score of 10.31 (SD = 1.80), equivalent to 68.67%. Among the domains, Relational-Reflective obtained the highest mean (M = 11.13, SD = 1.72, 74%), followed by Creative-Identity (M = 10.24, SD = 1.74, 68%), and Writing Essentials (M = 9.56, SD = 1.93, 64%). The results show that while students demonstrated competence in reflecting on their writing, considering audience, and applying feedback, their performance in creative-identity tasks such as adapting writing styles and maintaining originality was slightly lower. Writing Essentials, which included the use of academic vocabulary and synthesis of sources, received the lowest ratings, suggesting that technical writing elements remain the most challenging area. Overall, the findings confirm that students are performing at a proficient level across all three domains of situated academic writing. However, the results also indicate that further development is needed before they can progress to the advanced proficient level, particularly in strengthening foundational writing skills. Table 3. Level of proficiency of the respondents in terms of Situated Academic Writing as rated by inter-raters Situated Academic Writing Sum Mean SD % Level of Proficiency Writing Essentials 516 9.56 1.93 64% P Relational-Reflective 601 11.13 1.72 74% P Creative-Identity 553 10.24 1.74 68% P Overall 556.67 10.31 1.80 68.67% P Legend : Mean Descriptive Interpretation (DI) 12.01 – 15.00 Advanced Proficient (AdvP) 9.01 – 12.00 Proficient (P) 6.01 – 9.00 Approaching Proficient (AP) 3.01 – 6.00 Developing Proficient (DP) 1.00 – 3.00 Beginning Proficient (BP) 5.3 Inferential Analysis Across Variables Table 4 shows the inferential statistical analyses conducted in this study aimed to examine the relationship between AI literacy and the perceived level of situated academic writing among first-year English major students. First, the correlations across the three domains of situated academic writing showed positive and significant associations. The strongest relationship was found between Creative-Identity and Writing Essentials (r = 0.77), followed by Creative-Identity and Relational-Reflective (r = 0.67). These strong correlations suggest that students who view themselves as creative writers also perceive themselves as capable in foundational skills and reflective practices. Second, the analysis of associations across AI literacy domains revealed very strong relationships. In particular, Critical Appraisal and Practical Application recorded the highest correlation (r = 0.84), indicating that students who are aware of ethical and evaluative aspects of AI also demonstrate competence in applying AI concepts in real-life contexts. This finding reflects the close connection between critical awareness and practical use of emerging technologies. In contrast, correlations involving Technological Understanding tended to be weaker compared to other AI literacy components. For instance, Technological Understanding and Relational-Reflective showed a moderate association (r = 0.45), while Technological Understanding and Practical Application recorded a similarly modest result (r = 0.45). These findings suggest that while students’ technical grasp of AI concepts is linked to their academic writing self-efficacy, the relationship is not as strong as that observed in other domains. Overall, the correlation analysis confirms that AI literacy is positively associated with perceived levels of situated academic writing. Strong to very strong relationships across domains demonstrate that students’ confidence in ethical and applied aspects of AI is closely connected to their self-assessed writing abilities, while weaker correlations with technological understanding indicate an area for further development. Table 4. Test for significant relationship between the level of AI literacy and perceived level of situated academic writing of the respondents Factors Mean WE Mean RR Mean CI Mean TU Mean CA Mean PA Mean WE 1 Mean RR 0.6513 1 Mean CI 0.7734 0.6696 1 Mean TU 0.6118 0.4474 0.6026 1 Mean CA 0.5150 0.5682 0.5350 0.6635 1 Mean PA 0.4689 0.6186 0.5013 0.4492 0.8384 1 Legend: WE-Writing Essentials, RR-Relational-Reflective, CI-Creative-Identity, TU-Technological Understanding, CA-Critical Appraisal, PA-Practical Application r Value Range Interpretation ±0.00 to ±0.10 Negligible or no correlation ±0.10 to ±0.39 Weak correlation ±0.40 to ±0.59 Moderate correlation ±0.60 to ±0.79 Strong correlation ±0.80 to ±1.00 Very strong correlation 6. Experiences and Challenges in AI Literacy and Situated Academic Writing Analysis of the open-ended interview responses revealed several key challenges that contribute to students’ difficulties with AI literacy and academic writing. A commonly reported issue was limited exposure to AI concepts, with respondents noting unfamiliarity with terms such as machine learning , big data , and neural networks . This lack of foundational understanding reflects gaps in technological knowledge and helps explain lower scores in the Technological Understanding domain. Some participants also expressed misconceptions about how AI works, indicating uncertainty in distinguishing between its mechanisms and applications. Another challenge centered on academic writing skills, particularly difficulties with structuring essays and using formal vocabulary. Several students reported nervousness and a lack of confidence when tasked with longer writing assignments, pointing to persistent struggles in applying academic conventions. Respondents also described challenges in selecting and appropriately using scholarly terms, which often led to hesitation and self-doubt in their written work. These issues align with the relatively lower performance observed in Writing Essentials. Despite these challenges, the analysis also uncovered promising opportunities. Many students expressed curiosity and a strong desire to deepen their knowledge of AI, with several indicating interest in learning both the technical and ethical aspects of AI use. This openness suggests a willingness to engage with AI in responsible and effective ways. Respondents also recognized the potential of AI tools to support their academic writing, provided they gain proper understanding of how such tools function. Furthermore, the expressed motivation to improve writing structure and vocabulary points to a readiness to benefit from the specific interventions, such as structured writing workshops and guided AI literacy programs. Overall, the findings reveal that while students face significant challenges in both AI literacy and academic writing, their eagerness to learn and improve offers valuable opportunities for skill development. With appropriate support, these students may not only strengthen their writing proficiency but also cultivate a more comprehensive and responsible engagement with AI technologies. 7. Inputs to Writing Intervention Program Based on the results of the study, the intervention program WritIntel: Strengthening Writing Essentials and AI Technological Understanding among English Major Students was designed. The findings revealed that among the dimensions of AI literacy, Technological Understanding obtained the lowest composite mean (M = 3.16, SD = 0.67, Moderate), suggesting that while students show awareness of AI, they lack deeper comprehension of how AI systems function—particularly in relation to neural networks, supervised learning, and machine learning. Similarly, in situated academic writing, Writing Essentials recorded the lowest scores (M = 3.59, 64% proficiency), indicating difficulties in synthesizing sources, using academic vocabulary, and overcoming writing challenges. These results provide the basis for a structured program that integrates academic writing instruction with AI concept enhancement. The content of the program is organized into five core modules. Module 1 introduces AI fundamentals through interactive lectures on neural networks, reinforcement learning, and decision-making systems. Module 2 focuses on writing essentials through hands-on workshops in vocabulary use, paraphrasing, synthesis, and structuring academic arguments. Module 3 links both strands by assigning integrated writing tasks where students explain AI concepts in their own words. Module 4 introduces students to AI-supported writing tools such as Grammarly, Quillbot, Gemini, and ChatGPT, with attention to ethical use and limitations. Finally, Module 5 incorporates peer and instructor feedback, enabling students to revise drafts and reflect on their learning through journals. Together, these modules are designed to strengthen technical understanding while enhancing foundational writing skills. The program is set for a duration of eight weeks, with two one-hour sessions per week. The participants include English major students at Romblon State University who demonstrated moderate AI literacy in Technological Understanding and lower proficiency in Writing Essentials. To support engagement, the program combines lectures, practice-based writing activities, and peer collaboration. Progress will be monitored through weekly attendance, task submissions, and mid-program feedback surveys, ensuring that participants remain on track toward the objectives. Importantly, WritIntel is designed as a sustainable initiative. It will be implemented yearly for all incoming first-year students and will continue until their third year, allowing results to be tracked and monitored annually to assess long-term effectiveness. Evaluation of the program will adopt both quantitative and qualitative approaches. Pre- and post-tests will measure gains in AI knowledge, with an expected 15% increase as an indicator of improvement. Writing performance will be assessed through rubric-based scoring, with a target of at least one proficiency level increase. Reflection journals, exit surveys, and a possible focus group discussion will provide further insights into students’ experiences and confidence levels. By combining structured instruction, practical activities, continuous feedback, and a sustainable yearly implementation plan, the WritIntel program aims not only to address current gaps but also to ensure lasting improvement in AI literacy and academic writing skills across cohorts of English major students. 8. Discussion This study provides important insights into the relationship between Artificial Intelligence (AI) literacy and situated academic writing among English major students at Romblon State University, revealing both areas of strength and dimensions requiring further development. While students demonstrated confidence in ethical and applied aspects of AI, as well as reflective and creative domains of writing, the results also identified persistent challenges in technical understanding and foundational writing skills. The findings suggest that students are aware of the role of AI and writing in academic contexts, yet still face barriers in technical comprehension and mechanics. This gap reflects a pattern also observed in prior research: positive attitudes and awareness that are not always matched by deeper conceptual mastery or writing competence. One of the most notable findings concerns students’ low scores in Technological Understanding (M = 3.16, SD = 0.67), which indicates limited grasp of AI concepts such as neural networks, supervised learning, and machine learning. This is consistent with Laupichler et al. [ 22 ] and Juma [ 19 ], who reported similar deficiencies in students’ technical comprehension despite widespread awareness of AI. In contrast, students scored highly in Critical Appraisal (M = 3.69) and Practical Application (M = 3.60), demonstrating awareness of data privacy, ethical responsibility, and the use of AI tools in academic and real-world settings. This finding echoes Wang et al. [ 39 ], who likewise observed strong ethical literacy but conceptual gaps in AI knowledge. These results indicate that students may be engaging with AI tools pragmatically without fully understanding the underlying systems, reinforcing the need for experiential instruction that bridges practice and theory. In terms of situated academic writing, students rated themselves moderately proficient in Writing Essentials (M = 3.59), while showing higher self-confidence in Relational-Reflective (M = 3.98) and Creative-Identity (M = 3.74). Inter-rater evaluations supported this trend, rating students as “Proficient” across all domains, with the highest scores in Relational-Reflective (M = 11.13, 74%) and lower performance in Writing Essentials (M = 9.56, 64%). This convergence suggests that students’ self-awareness of their writing strengths and weaknesses is generally reliable. However, slight discrepancies emerged: students tended to overestimate their reflective abilities, while underestimating their foundational skills. This aligns with Sehlström et al. [ 35 ], who noted that high self-efficacy does not always equate to higher performance, and Jalaluddin et al. [ 17 ], who found that students with lower confidence sometimes achieve stronger outcomes. These findings explain the complexity of self-efficacy in writing and the need for structured feedback to calibrate students’ self-perceptions. The correlation analysis further revealed a moderate to strong positive relationship between AI literacy and situated academic writing, particularly between Technological Understanding and Writing Essentials (r = 0.61), as well as between Creative-Identity and both reflective and foundational domains. This suggests that students who are more confident in their AI comprehension tend also to show stronger writing performance, especially in tasks requiring synthesis and originality. These results are in line with Bekturova et al. [ 5 ], who argue that digital competencies can enhance confidence and academic performance. However, the qualitative findings revealed challenges that temper these positive associations. Students expressed difficulty with AI terminology, nervousness in using academic vocabulary, and uncertainty in structuring essays. This highlights a potential disconnect: while correlations suggest a reinforcing relationship, actual learning experiences expose continuing struggles that need specific intervention. Despite these challenges, the study also uncovered significant opportunities. Students expressed eagerness to improve their AI knowledge and writing competence, with many acknowledging that stronger literacy in both areas would benefit their academic growth. This openness reflects findings by Wood et al. [ 40 ]. At the same time, the sustainability of these efforts must be ensured through structured programs, such as the proposed WritIntel intervention, which integrates AI literacy with writing instruction and is designed for yearly implementation. By systematically addressing gaps in technological understanding and writing essentials while reinforcing reflective and creative strengths, such interventions can provide long-term benefits for multiple cohorts of English major students. Finally, situating these findings in broader contexts reveals that the challenges observed are not unique to Romblon State University. Similar patterns of strong ethical awareness but weak technical understanding have been documented in both local [ 3 ][ 30 ] and international [ 15 ][ 36 ] contexts. Likewise, struggles in foundational writing skills amid confidence in reflective and creative expression have been noted by [ 13 ][ 18 ]. This regional and global consistency proves the need for educational strategies that integrate technical, ethical, and practical dimensions of AI literacy with socially situated approaches to academic writing. Cross-institutional collaboration, curriculum integration, and sustainable interventions can help bridge these gaps, ensuring that students not only engage with AI tools and writing practices but also master the underlying knowledge and skills necessary for advanced academic and professional competence. 9. Limitations While this study makes valuable contributions to understanding the relationship between AI literacy and situated academic writing, several limitations must be acknowledged. First, the reliance on self-reported perceptions for AI literacy and writing self-efficacy raises the possibility of social desirability bias, where students may have overestimated their competence or downplayed difficulties to align with perceived academic expectations. This reliance limits the precision of the findings, as students’ actual comprehension and performance may not fully match their reported confidence. Second, the study was conducted with English major students from a single institution, which narrows the generalizability of the results to broader student populations. Since AI exposure and writing practices can vary across disciplines, universities, and regional contexts, further research in diverse academic and institutional settings is needed to validate and extend these findings. Third, while inter-rater evaluations were used to assess situated academic writing proficiency, the study did not incorporate direct performance-based assessments of AI literacy, such as task-based evaluations or practical demonstrations of AI tool usage. Including such measures in future work would provide a more objective account of students’ abilities and complement the self-assessment data. Future research should adopt mixed-methods approaches, integrating surveys with classroom observations, writing portfolio analyses, and task-based AI literacy assessments to build a more comprehensive understanding. Longitudinal studies are also recommended to examine how students’ AI literacy and writing proficiency evolve across their undergraduate years, especially as programs like WritIntel are sustained and implemented annually. Such designs would capture both immediate gains and long-term developmental trajectories, offering stronger evidence for the effectiveness of integrated interventions. 10. Conclusions This study highlights both the potential of strengthening artificial intelligence (AI) literacy and situated academic writing among English major students at Romblon State University, as well as the challenges that remain in bridging technical knowledge and foundational writing skills. While students demonstrated strong competence in critical appraisal and practical application—particularly in understanding ethical implications, data privacy, and the relevance of AI in daily life—their technological understanding of core concepts such as machine learning and neural networks was only moderate. In parallel, students reported confidence in relational-reflective and creative-identity writing, showing preparedness in audience awareness, originality, and the use of feedback, yet continued to face difficulties in writing essentials such as source synthesis and academic vocabulary. Inter-rater evaluations confirmed overall proficiency across writing domains, though foundational skills still require focused improvement. These findings also revealed moderate to strong correlations between AI literacy and writing performance, indicating that greater digital literacy, especially in technical and ethical areas, supports stronger academic writing competence. Importantly, the results suggest that enhancing AI literacy and academic writing should be pursued as a sustained process rather than a one-time intervention. The proposed WritIntel program responds directly to these needs, integrating instruction in technological understanding and writing essentials through a structured, multi-component design. As a sustainable initiative, the program will be implemented annually for first-year students and monitored through their undergraduate years, allowing progress to be tracked up to the third year. By aligning AI knowledge with academic writing practice, the program provides a long-term pathway for students to advance from proficiency to higher competence levels, ensuring they are equipped to meet the demands of academic coursework and to thrive in digital and knowledge-driven environments. 11. Recommendations The researchers recommend the following: Capacity building in AI literacy and writing competence Strengthening students’ academic performance requires structured programs that integrate AI literacy with foundational writing skills. Institutions should design hands-on, practice-based workshops focused on technological understanding of AI concepts alongside training in writing essentials such as source synthesis, academic vocabulary, and argument organization. Regular assessments and writing clinics should be implemented to track growth, while peer-learning groups and writing circles can provide sustained collaborative support. Sustainable program implementation and resource support To ensure long-term impact, the WritIntel program should be institutionalized as a yearly intervention for all first-year English major students, with continuous monitoring until their third year. Adequate resources—such as access to AI tools, writing-enhancement software, and updated instructional materials—must be provided to support program activities. Faculty should also be trained to integrate AI content into coursework and to guide students in responsible, ethical use of AI in academic writing. Institutional alignment and collaborative partnerships For effectiveness and sustainability, the intervention program should be fully aligned with Romblon State University’s educational policies and curriculum frameworks. Establishing partnerships with AI practitioners, professional organizations, and educational agencies will strengthen technical and instructional resources. Collaborative efforts will also enable broader dissemination of best practices, equitable resource allocation, and the scaling of successful strategies across departments and related programs. Declarations Acknowledgements We thank the Romblon State University-College of Education (RSU-CED) for hosting the authors during this study. Author Contributions The task distribution for the final manuscript development is as follows: RBG and AMG contributed to the writing of Introduction and Literature Review; CS contributed to the writing of Methodology; CH and SA contributed to the writing of Findings and Conclusion; MS contributed to the writing of Discussion; Formatting and Reference Management were done by RBG and AMG; and Proofreading and Final Editing of the manuscript were done by MS. All authors read and approved the final manuscript. Funding This research received no funding. Data Availability Because of reasons of sensitivity and protection of anonymity, the data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate: This study received ethical approval for its general characteristics from the Romblon State University - College of Education. This ensured that careful consideration of research ethics principles, potential risks to participants, informed consent procedures, and data protection measures was made prior to the conduct of the study in conformity with the standard ethical guidelines. The authors confirm that informed consent was obtained from all participants prior to their participation in the study to ensure their voluntary participation. No participants under the age of 16 were involved in the study. Consent for Publication Not applicable Competing Interests The authors declare no competing interests. References Akkaya, A. & Aydin, G. (2018). Academics' Views on the Characteristics of Academic Writing. Educational Policy Analysis and Strategic Research, 13(2), 128-160. doi: 10.29329/epasr.2018.143.7 Alzubi, A. A. F. (2024). Generative artificial intelligence in the EFL writing context: Students' literacy in perspective. Qubahan Academic Journal, 4 (2), 59-69. https://doi.org/10.48161/qaj.v4n2a506. Asio, J.M. 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Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human–Computer Interaction , 1-23. https://doi.org/10.1080/10447318.2024.2383033 Wood, E., Ange, B., & Miller, D. D. (2021). Are we ready to integrate artificial intelligence literacy into medical school curriculum: Students and faculty survey. Journal of Medical Education and Curricular Development, 8. https://doi.org/10.1177/238212. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eAccording to Moreno, Korzeniowski, and Esp\u0026oacute;sito [29], writing is more than a cognitive activity; it is a process of thinking that involves organizing, revising, and refining ideas to produce a written output subject to evaluation. In the same vein, Akkaya \u0026amp; Aydin [1] emphasizes that academic writing is not only about following genre conventions but also about applying specific principles for reporting and textualizing scientific findings. These perspectives explain the complexity of writing as both a cognitive and social endeavor.\u003c/p\u003e\n\u003cp\u003eRecent scholarship has also begun to examine the intersection between artificial intelligence (AI) literacy and writing. For instance, Alzubi [2] found that 278 EFL learners were literate in generative AI tools, which enhanced their English writing skills. Similarly, Hamouma and Menezla [14] concluded that digital literacy significantly improves academic writing performance among EFL students. However, these studies often focus on writing in general and overlook the importance of context-specific or situated writing tasks.\u003c/p\u003e\n\u003cp\u003eSituated academic writing connects writing practices to broader social and disciplinary contexts. Mitchell [28] argues that successful academic writing involves not only individual strategies but also engagement with disciplinary norms and identity-building. Par\u0026eacute; [33] likewise highlights that writing helps uphold community values and facilitates negotiation of meaning between writer and reader. These insights suggest that writing instruction must account for the situated and social nature of academic tasks rather than treating writing as an isolated skill.\u003c/p\u003e\n\u003cp\u003eDespite this, gaps remain in understanding how ESL undergraduates navigate these situated processes. Much of the foundational work on writing strategies, such as the studies by Torrance, Thomas, and Robinson [37], was conducted with native English speakers. Their findings identified strategies ranging from detailed planning and brainstorming to minimal drafting and mental outlining. Yet, few investigations have explored how these strategies manifest among ESL learners in context-specific writing scenarios.\u003c/p\u003e\n\u003cp\u003eIn the Philippines, interest in AI tools among students is rising. Instructure [16] reported that over 83% of Filipino students use AI for academic purposes, particularly in research and writing. However, the Descartes Institute [11] report revealed that the country ranked 82nd out of 124 in digital skills. Similarly, Oxford Insights [31] placed the Philippines 65th out of 193 in AI readiness, citing gaps in human capital. Ligot [24] further noted that limitations in education, training, and infrastructure continue to hinder the country\u0026rsquo;s ability to adopt AI technologies effectively.\u003c/p\u003e\n\u003cp\u003eAlthough studies have examined AI literacy and academic writing separately, little is known about how students integrate AI into their academic writing, particularly in contexts that demand critical thinking and disciplinary awareness. Teachers have observed that while students frequently use AI tools, many struggle to apply them meaningfully in writing tasks. This disconnect shows the need for research exploring the intersection of AI literacy and situated academic writing in actual classroom practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1 Purpose and Research Questions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis information prompted the researchers to pursue a study on how the artificial intelligence literacy of first-year English major students affect their situated academic writing skills at Romblon State University, Philippines. Specifically, the study sought answers to the following questions:\u003c/p\u003e\n\u003cp\u003e1. What is the level of artificial intelligence literacy of the respondents in terms of:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;a. technological understanding,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;b. critical appraisal, and\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;c. practical application\u003c/p\u003e\n\u003cp\u003e2. What is the perceived level of situated academic writing self-efficacy of the respondents in terms of:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;a. writing-essentials,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;b. relational-reflective, and\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;c. creative-identity?\u003c/p\u003e\n\u003cp\u003e3. What is the level of situated academic writing proficiency of respondents as evaluated by inter-raters in terms of:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;a. writing-essentials,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;b. relational-reflective, and\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;c. creative-identity?\u003c/p\u003e\n\u003cp\u003e4. Is there any significant relationship between the level of AI literacy and perceived level of situated academic writing of the respondents?\u003c/p\u003e\n\u003cp\u003e5. What are the experiences and challenges of the respondents with low AI literacy, and low perceived levels of situated academic writing?\u003c/p\u003e\n\u003cp\u003e6. What intervention program can be proposed based on the results of the study?\u003c/p\u003e"},{"header":"2.\tTheoretical Background","content":"\u003cp\u003eThis study adopts Situated Learning Theory (SLT) as its central framework to examine the development of artificial intelligence (AI) literacy and situated academic writing among first-year English major students. Proposed by Lave and Wenger [23], SLT views learning as a social process that occurs through participation in a community of practice. According to the theory, learners begin at the periphery of the community through Legitimate Peripheral Participation (LPP) and gradually move toward full participation as they acquire knowledge, skills, and confidence. Learning is therefore embedded within authentic activities, social interactions, and cultural contexts, rather than isolated from them.\u003c/p\u003e\n\u003cp\u003eIn the context of this study, students\u0026rsquo; acquisition of AI literacy and writing proficiency can be understood as participation in overlapping academic and technological communities of practice. Novices, represented by first-year students, initially demonstrate limited knowledge of technical AI concepts and foundational writing skills. Through engagement with peers, instructors, and AI tools, they progressively build competence in technological understanding, critical appraisal, and practical application, alongside writing-essentials, relational-reflective, and creative-identity domains. These practices involve not only individual skill-building but also interaction with artifacts such as AI-assisted drafts, grammar checkers, and structured feedback, which shape students\u0026rsquo; learning trajectories.\u003c/p\u003e\n\u003cp\u003eSituated Learning Theory explains the importance of context and participation in fostering both AI literacy and academic writing. It suggests that students\u0026rsquo; movement from peripheral to central participation depends on meaningful engagement with authentic writing tasks, guided practice, and collaborative reflection. By situating learning within real academic challenges and AI-supported activities, the framework highlights how students can develop competence that is both practical and socially grounded.\u003c/p\u003e"},{"header":"3. Literature Review","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Artificial Intelligence Literacy\u003c/h2\u003e\u003cp\u003eThe term \u0026ldquo;AI Literacy\u0026rdquo; was first introduced by Burgsteiner, Kandlhofer, and Steinbauer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] to describe the core competencies required to understand fundamental AI concepts. Building on this, Long and Magerko [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] defined AI literacy as the ability to critically evaluate AI, collaborate with it, and use it responsibly across diverse contexts. Kong, Cheung, and Zhang [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] further categorized AI literacy into three components: understanding AI fundamentals, applying AI in assessment, and solving real-world problems using AI concepts.\u003c/p\u003e\u003cp\u003eAsio [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] emphasized AI literacy as both comprehension and application of AI\u0026rsquo;s principles and implications, while Wang, Rau, and Yuan [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] explained its role in recognizing AI in everyday contexts, using it effectively, and critically assessing AI-generated information within an ethical framework. Together, these perspectives highlight AI literacy as a multidimensional construct that goes beyond technical skills to include evaluative and ethical dimensions.\u003c/p\u003e\u003cp\u003eGiven the increasing prominence of AI, it is crucial for individuals\u0026mdash;particularly college students, who represent the next generation of professionals and leaders\u0026mdash;to cultivate AI literacy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, empirical studies reveal mixed results. Gokce et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] reported moderate literacy in critical appraisal (CA) and practical application (PA), but low performance in technological understanding (TU), with variations linked to gender and AI usage. Similarly, Asio [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] found moderately high levels of literacy among tertiary students, influenced by academic performance, year level, and gender. Laupichler et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] observed strong CA and PA but weak TU, while Juma [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] reported that students valued AI yet lacked both conceptual understanding and trust in using it for academic evaluation.\u003c/p\u003e\u003cp\u003eOther studies reinforce this variability. Obenza et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] found high overall literacy among Davao students. Asirit and Hua [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] revealed that while 79.2% of students were aware of AI, 21.2% had little knowledge, highlighting the need for stronger AI education. Hornberger et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and Teng et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] both showed stronger literacy among STEM students and those with prior AI experience. Similarly, Cubas and Ersdal [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] identified a persistent gap between students\u0026rsquo; tool usage and their theoretical understanding.\u003c/p\u003e\u003cp\u003eAt the institutional level, Wood et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] noted that students and faculty demonstrated limited AI knowledge despite showing strong interest in training. In China, Wang et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] found students exhibited high ethical AI literacy but continued to show conceptual gaps. Collectively, these studies illustrate that AI literacy remains uneven, shaped by background, exposure, and academic discipline, suggesting the need for specific educational interventions.\u003c/p\u003e\u003cp\u003eDespite these advances, there remains a lack of consensus on how AI literacy should be systematically developed across higher education curricula in the Philippines. While several studies have examined awareness levels and usage patterns, fewer have investigated how AI literacy interacts with domain-specific competencies, such as situated academic writing. Moreover, most existing research has focused on technologically advanced contexts, leaving developing countries underrepresented. This imbalance asserts the importance of investigating AI literacy in settings like the Philippines, where students demonstrate high interest in AI but face persistent gaps in digital readiness. Understanding these dynamics is crucial for designing programs that not only raise awareness of AI but also empower students to integrate it meaningfully into their academic and professional practices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Situated Academic Writing\u003c/h2\u003e\u003cp\u003eSituated academic writing emphasizes how writing practices are embedded in broader social, cultural, and institutional contexts. Green [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] highlighted that writing is not only an individual skill but also a socio-cognitive process shaped by interaction with texts, genres, and disciplinary communities. Successful student writers engage deeply with rhetorical conventions and disciplinary expectations, which supports their integration into academic communities.\u003c/p\u003e\u003cp\u003eSala-Bubar\u0026eacute; and Castell\u0026oacute; [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] further noted the importance of writing regulation, calling for approaches that consider both emotional and contextual influences in higher education writing. Their study revealed that writing difficulties often stem not just from technical skills but also from students\u0026rsquo; struggles to regulate their emotions and manage contextual demands. Such findings emphasize the need for pedagogical strategies that support students\u0026rsquo; holistic development as writers.\u003c/p\u003e\u003cp\u003eFurther, Lillis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] introduced the academic literacies perspective, which frames writing as a socially situated and ideologically shaped practice. Rather than reducing writing to technical correctness, this approach emphasizes identity, power, and epistemology in shaping students\u0026rsquo; academic texts. By adopting a dialogic pedagogy, educators can foster deeper engagement and critical interaction, positioning students as active participants in knowledge construction.\u003c/p\u003e\u003cp\u003eMore recent work has expanded on these perspectives by examining the role of collaboration and dialogue in academic writing development. Bouwer and van der Veen [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] demonstrated that dialogic writing interventions in upper elementary education improved students\u0026rsquo; ability to negotiate meaning and revise their texts effectively. These findings suggest that academic writing instruction should integrate dialogic and collaborative activities that mirror authentic academic practices.\u003c/p\u003e\u003cp\u003eFinally, collaborative digital writing contexts provide additional insights into situated academic writing. Paba Argote et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] analyzed how students engaged in digital argumentative writing and found that dialogic regulation such as peer feedback, negotiation, and co-construction was central to their progress. Such studies show that situated writing is dynamic, socially negotiated, and increasingly mediated by digital tools [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Together, this body of work affirms that academic writing must be taught as a situated, interactive practice shaped by discourse communities, collaboration, and evolving technological environments.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThis study employed a mixed-methods research design to comprehensively investigate the relationship between Artificial Intelligence (AI) literacy and situated academic writing among first-year English major students at RSU-College of Education. By integrating both quantitative and qualitative approaches, the study sought to generate a deeper and more specific understanding of students\u0026rsquo; literacy in AI, their confidence and performance in situated academic writing, and the challenges they encounter in developing these competencies. The design enabled triangulation of results, thereby enhancing both the breadth and depth of interpretation.\u003c/p\u003e\u003cp\u003eA total of 54 first-year students enrolled during the Academic Year 2024\u0026ndash;2025 participated in the study. The respondents were divided into two blocks, with Block 1 consisting of 25 students and Block 2 consisting of 29 students. This sample size was deemed appropriate for the scope of the study, providing sufficient representation to explore relationships between AI literacy and situated academic writing proficiency.\u003c/p\u003e\u003cp\u003eTo measure AI literacy, the Scale for the Assessment of Non-Experts\u0026rsquo; AI Literacy (SNAIL) developed by Laupichler et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] was administered. The instrument consists of 31 items rated on a 5-point Likert scale and covers three dimensions: Technological Understanding (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.93), Critical Appraisal (α\u0026thinsp;=\u0026thinsp;.91), and Practical Application (α\u0026thinsp;=\u0026thinsp;.85). The overall reliability coefficient of the SNAIL is .92, indicating excellent internal consistency. The instrument required approximately 30 minutes to complete. To assess the students\u0026rsquo; perceived level of situated academic writing, the Situated Academic Writing Self-Efficacy Scale (SAWSES) by Mitchell et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was used. This scale consists of 16 items divided into three dimensions: Relational-Reflective Writing (α\u0026thinsp;=\u0026thinsp;.88), Creative Identity (α\u0026thinsp;=\u0026thinsp;.88), and Writing Essentials (α\u0026thinsp;=\u0026thinsp;.81). The overall Cronbach\u0026rsquo;s alpha for the SAWSES is .94, and the instrument could be completed in approximately 15 minutes.\u003c/p\u003e\u003cp\u003eTo measure actual proficiency in situated academic writing, participants were tasked with writing an essay on multilingualism based on a contextualized scenario and guided questions. Students were instructed to connect their discussion to one of four Sustainable Development Goals (SDGs) provided by the researchers. The outputs were evaluated by three independent raters using a rubric developed by the researchers to ensure systematic assessment and reliability of scores.\u003c/p\u003e\u003cp\u003eQuantitative data were analyzed using SPSS Version 25. Descriptive statistics such as weighted mean and standard deviation were calculated to summarize the levels of AI literacy and perceived situated academic writing. Pearson\u0026rsquo;s Rho Correlation was used to determine the strength and direction of the linear relationship between the two constructs. Prior to interpretation, assumptions for correlation were examined to ensure the appropriateness of the analyses. Where relevant, the findings were further contextualized with the support of effect size interpretation to indicate the practical significance of results.\u003c/p\u003e\u003cp\u003eQualitative data were derived from semi-structured interviews focusing on students with low AI literacy and low perceived levels of situated academic writing, specifically in the domains of Technological Understanding and Writing Essentials. Interview responses were analyzed using Braun and Clarke\u0026rsquo;s [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] six-phase thematic analysis approach, which involved familiarization with the data, generating initial codes, identifying patterns, reviewing and refining themes, and reporting the findings. An inductive coding approach was employed, allowing insights to emerge directly from participants\u0026rsquo; accounts.\u003c/p\u003e\u003cp\u003eThematic analysis yielded three core themes: (1) Issues on Understanding and Awareness of AI Terminology, (2) Curiosity and Desire to Learn More About AI, and (3) Challenges in Academic Writing and Use of Formal Language. These themes provided contextualized insights into students\u0026rsquo; difficulties and aspirations, complementing the quantitative findings and offering a holistic view of the relationship between AI literacy and situated academic writing.\u003c/p\u003e"},{"header":"5. Findings","content":"\u003cp\u003eThis study generated important insights into the levels of AI literacy and situated academic writing among first-year English major students at RSU-College of Education. The results are organized in line with the research questions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 Level of Artificial Intelligence (AI) literacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results in Table 1 indicate that students demonstrated a moderate level of technological understanding (M = 3.16, SD = 0.67), with most respondents reporting occasional ability to explain or describe AI concepts. Items such as explainable AI (M = 3.33, SD = 0.78) and the difference between general and narrow AI (M = 3.32, SD = 0.93) showed relatively higher means, while concepts such as artificial neural networks (M = 2.82, SD = 0.87) and big data (M = 2.96, SD = 0.82) were rated lower, suggesting variability in familiarity with technical aspects.\u003c/p\u003e\n\u003cp\u003eIn contrast, critical appraisal recorded a high composite mean (M = 3.69, SD = 0.64). Students consistently reported confidence in identifying ethical issues (M = 3.43, SD = 0.92), describing potential risks (M = 3.83, SD = 0.86), and explaining the importance of data privacy (M = 3.89, SD = 0.77). The highest rating was for describing what artificial intelligence is (M = 3.93, SD = 0.82), showing that most respondents could clearly define and reflect on AI-related concerns.\u003c/p\u003e\n\u003cp\u003ePractical application also obtained a high composite mean (M = 3.60, SD = 0.65). Students reported frequent ability to recognize AI in daily life (M = 3.82, SD = 0.87), explain its growing importance (M = 3.80, SD = 0.81), and identify technical applications supported by AI (M = 3.54, SD = 0.95). Lower means were recorded for naming applications related to natural language processing (M = 3.33, SD = 0.75) and critically evaluating implications in daily routines (M = 3.45, SD = 0.81), suggesting some variation in applied experiences.\u003c/p\u003e\n\u003cp\u003eOverall, the results show that while students demonstrated high levels in critical appraisal and practical application, their technological understanding remained moderate, indicating a need for further development in explaining and analyzing core AI concepts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Level of Artificial Intelligence Literacy among English major students at Romblon State University\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTECHNOLOGICAL UNDERSTANDING\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eI can\u0026hellip;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col\u003e\n \u003cli\u003edescribe how machine learning models are trained, validated and tested.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eexplain how deep learning relates to machine learning.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003eexplain how rule-based systems differ from machine learning systems.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003eexplain how AI applications make decisions.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003eexplain how \u0026ldquo;reinforcement learning\u0026rdquo; works on basic level.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003eexplain the difference between general and narrow artificial intelligence\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003eexplain how sensors are used by computers to collect data and can be used for AI purposes.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"8\"\u003e\n \u003cli\u003eexplain what \u0026ldquo;artificial neural network\u0026rdquo; means.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"9\"\u003e\n \u003cli\u003eexplain how machine learning works at general level.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"10\"\u003e\n \u003cli\u003eexplain the difference between supervised learning and unsupervised learning.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"11\"\u003e\n \u003cli\u003edescribe the concept of explainable AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"12\"\u003e\n \u003cli\u003eexplain how some AI systems can act and react in their environment.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"13\"\u003e\n \u003cli\u003edescribe the concept of big data.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"14\"\u003e\n \u003cli\u003eevaluate whether media representations of AI go beyond its actual use.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRITICAL APPRAISAL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eI can\u0026hellip;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"15\"\u003e\n \u003cli\u003eexplain why data privacy must be considered when developing and using AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"16\"\u003e\n \u003cli\u003eexplain why data security must be considered when developing and using AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"17\"\u003e\n \u003cli\u003eidentify ethical issues surrounding AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"18\"\u003e\n \u003cli\u003edescribe risks that may arise when using AI systems.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"19\"\u003e\n \u003cli\u003ename weaknesses of AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"20\"\u003e\n \u003cli\u003edescribe potential legal problems that may arise when using AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"21\"\u003e\n \u003cli\u003ecritically reflect on the potential impact of AI on individual and society.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"22\"\u003e\n \u003cli\u003edescribe why humans play an important role in the development of AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"23\"\u003e\n \u003cli\u003edescribe why data play an important role in the development of AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"24\"\u003e\n \u003cli\u003edescribe what artificial intelligence is.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRACTICAL APPLICATION\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eI can\u0026hellip;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"25\"\u003e\n \u003cli\u003egive examples from my daily life where I might be contact with AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"26\"\u003e\n \u003cli\u003ename examples of technical applications that are supported by AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"27\"\u003e\n \u003cli\u003etell if technologies that I use is supported by AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"28\"\u003e\n \u003cli\u003eassess if the problem in my field can and should be solved by AI.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"29\"\u003e\n \u003cli\u003ename applications in which AI assisted natural language processing/understanding is used.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"30\"\u003e\n \u003cli\u003eexplain why AI has recently become increasingly important.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"31\"\u003e\n \u003cli\u003ecritically evaluate the implications of AI applications in at least one daily routine.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Descriptive Interpretation (DI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.00 - 1.80\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Very Low (VL)\u003c/p\u003e\n\u003cp\u003e1.81 - 2.60\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Low (L)\u003c/p\u003e\n\u003cp\u003e2.61 - 3.40\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Moderate (M)\u003c/p\u003e\n\u003cp\u003e3.41 - 4.20\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;High (H)\u003c/p\u003e\n\u003cp\u003e4.21 - 5.00 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Very High (VH)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Level of Situated Academic Writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results in Table 2 indicate that students perceived themselves to have a generally high level of situated academic writing self-efficacy (Grand Mean = 3.77, SD = 0.52). Among the three domains, Relational-Reflective recorded the highest composite mean (M = 3.98, SD = 0.45). Items such as considering the audience when writing (M = 4.50, SD = 0.61) and using feedback for improvement (M = 4.22, SD = 0.66) were rated very high, while other indicators such as creativity in writing (M = 4.07, SD = 0.75) and reflective practices (M = 4.07, SD = 0.70) also received high ratings, suggesting consistent confidence in this domain.\u003c/p\u003e\n\u003cp\u003eCreative-Identity also showed a high composite mean (M = 3.74, SD = 0.56). Students reported being able to adapt their writing to different assignments (M = 3.78, SD = 0.66) and recognize when they had diverged from audience needs (M = 3.91, SD = 0.68). They also expressed confidence in comfortably expressing academic concepts (M = 3.63, SD = 0.71) and balancing feedback with their own judgment (M = 3.76, SD = 0.73).\u003c/p\u003e\n\u003cp\u003eWriting Essentials recorded the lowest composite mean among the three domains, though it was still rated high (M = 3.59, SD = 0.54). Respondents indicated confidence in overcoming writing difficulties (M = 3.91, SD = 0.62), but lower ratings were observed in the ability to use scholarly academic words (M = 3.43, SD = 0.66) and synthesizing multiple sources (M = 3.44, SD = 0.72). These results suggest that while students demonstrate persistence in writing, technical aspects of academic style and integration of sources may remain areas of challenge.\u003c/p\u003e\n\u003cp\u003eOverall, the findings show that students expressed strong confidence in relational-reflective and creative-identity aspects of writing, while writing essentials scored comparatively lower. This pattern indicates that while students feel capable of adapting, reflecting, and developing a personal academic voice, they may still experience difficulty in areas requiring advanced academic language and synthesis of literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePerceived Level of Situated Academic Writing Self-Efficacy of English Major Students\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWRITING ESSENTIALS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col\u003e\n \u003cli\u003eEven when the writing is hard, I can find ways to overcome my writing difficulties.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eI can successfully use scholarly academic words and phrases when writing in my courses.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003eI can combine or synthesize multiple sources I\u0026rsquo;ve read to create an original product or text.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRELATIONAL-REFLECTIVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003eI can use creativity when writing an academic paper.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003eWhen I write, I can think about my audience and write so they clearly understand my meaning.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eVH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003eWhen I receive feedback on my writing, no matter how it makes me feel, I can use that feedback to improve my writing in the future.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eVH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003eWhen I reflect on what I am writing, I can make my writing better.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"8\"\u003e\n \u003cli\u003eI feel I can give my writing a creative spark and still sound professional.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"9\"\u003e\n \u003cli\u003eWhen I read articles about my topic, the connections I feel with the ideas of other authors can inspire me to express my own ideas in writing.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"10\"\u003e\n \u003cli\u003eWhen I look at the overall picture I\u0026rsquo;ve presented in my writing, I can assess how all the pieces tell the complete story of my topic or argument.\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"11\"\u003e\n \u003cli\u003eI feel I can develop my own writing voice (ways of speaking in my writing that are uniquely me).\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCREATIVE-IDENTITY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"12\"\u003e\n \u003cli\u003eEven with very specific assignment guidelines, I can find ways of writing my assignment to make it original or unique.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"13\"\u003e\n \u003cli\u003eI can comfortably express academic concepts, language, and values in my writing assignments.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"14\"\u003e\n \u003cli\u003eI can recognize when I\u0026rsquo;ve wandered away from writing what my audience needs to know and have begun writing about interesting, but unrelated, ideas.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"15\"\u003e\n \u003cli\u003eWith each new writing assignment, I can adapt my writing to meet the needs of that assignment.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003col start=\"16\"\u003e\n \u003cli\u003eWhen I seek feedback on my writing, I can decide when that feedback should be ignored or incorporated into a revision in my writing.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGRAND MEAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Descriptive Interpretation (DI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.00 - 1.80\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Very Low (VL)\u003c/p\u003e\n\u003cp\u003e1.81 - 2.60\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Low (L)\u003c/p\u003e\n\u003cp\u003e2.61 - 3.40\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Moderate (M)\u003c/p\u003e\n\u003cp\u003e3.41 - 4.20 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High (H)\u003c/p\u003e\n\u003cp\u003e4.21 - 5.00 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Very High (VH)\u003c/p\u003e\n\u003cp\u003eThe results in Table 3 indicate that students were rated as proficient in all three domains of situated academic writing, with an overall mean score of 10.31 (SD = 1.80), equivalent to 68.67%. Among the domains, Relational-Reflective obtained the highest mean (M = 11.13, SD = 1.72, 74%), followed by Creative-Identity (M = 10.24, SD = 1.74, 68%), and Writing Essentials (M = 9.56, SD = 1.93, 64%).\u003c/p\u003e\n\u003cp\u003eThe results show that while students demonstrated competence in reflecting on their writing, considering audience, and applying feedback, their performance in creative-identity tasks such as adapting writing styles and maintaining originality was slightly lower. Writing Essentials, which included the use of academic vocabulary and synthesis of sources, received the lowest ratings, suggesting that technical writing elements remain the most challenging area.\u003c/p\u003e\n\u003cp\u003eOverall, the findings confirm that students are performing at a proficient level across all three domains of situated academic writing. However, the results also indicate that further development is needed before they can progress to the advanced proficient level, particularly in strengthening foundational writing skills.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eLevel of proficiency of the respondents in terms of Situated Academic Writing as rated by inter-raters\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSituated Academic Writing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of Proficiency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eWriting Essentials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e64%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eRelational-Reflective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e11.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eCreative-Identity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e556.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.67%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend\u0026nbsp;:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Descriptive Interpretation (DI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e12.01 \u0026ndash; 15.00\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Advanced Proficient (AdvP)\u003c/p\u003e\n\u003cp\u003e9.01 \u0026ndash; 12.00\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Proficient (P)\u003c/p\u003e\n\u003cp\u003e6.01 \u0026ndash; 9.00\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Approaching Proficient (AP)\u003c/p\u003e\n\u003cp\u003e3.01 \u0026ndash; 6.00 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Developing Proficient (DP)\u003c/p\u003e\n\u003cp\u003e1.00 \u0026ndash; 3.00 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Beginning Proficient (BP)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Inferential Analysis Across Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 shows the inferential statistical analyses conducted in this study aimed to examine the relationship between AI literacy and the perceived level of situated academic writing among first-year English major students. First, the correlations across the three domains of situated academic writing showed positive and significant associations. The strongest relationship was found between Creative-Identity and Writing Essentials (r = 0.77), followed by Creative-Identity and Relational-Reflective (r = 0.67). These strong correlations suggest that students who view themselves as creative writers also perceive themselves as capable in foundational skills and reflective practices.\u003c/p\u003e\n\u003cp\u003eSecond, the analysis of associations across AI literacy domains revealed very strong relationships. In particular, Critical Appraisal and Practical Application recorded the highest correlation (r = 0.84), indicating that students who are aware of ethical and evaluative aspects of AI also demonstrate competence in applying AI concepts in real-life contexts. This finding reflects the close connection between critical awareness and practical use of emerging technologies.\u003c/p\u003e\n\u003cp\u003eIn contrast, correlations involving Technological Understanding tended to be weaker compared to other AI literacy components. For instance, Technological Understanding and Relational-Reflective showed a moderate association (r = 0.45), while Technological Understanding and Practical Application recorded a similarly modest result (r = 0.45). These findings suggest that while students\u0026rsquo; technical grasp of AI concepts is linked to their academic writing self-efficacy, the relationship is not as strong as that observed in other domains.\u003c/p\u003e\n\u003cp\u003eOverall, the correlation analysis confirms that AI literacy is positively associated with perceived levels of situated academic writing. Strong to very strong relationships across domains demonstrate that students\u0026rsquo; confidence in ethical and applied aspects of AI is closely connected to their self-assessed writing abilities, while weaker correlations with technological understanding indicate an area for further development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Test for significant relationship between the level of AI literacy and perceived level of situated academic writing of the respondents\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean WE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean RR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean TU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean CA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean PA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean WE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean RR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.6513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.7734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.6696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean TU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.6118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.4474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.6026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean CA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.5150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.5682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.5350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.6635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean PA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.4689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.6186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.5013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.4492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.8384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eLegend:\u0026nbsp;\u003c/em\u003eWE-Writing Essentials, RR-Relational-Reflective, CI-Creative-Identity, TU-Technological Understanding, CA-Critical Appraisal, PA-Practical Application\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003er Value Range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026plusmn;0.00 to \u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eNegligible or no correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026plusmn;0.10 to \u0026plusmn;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eWeak correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026plusmn;0.40 to \u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eModerate correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026plusmn;0.60 to \u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eStrong correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026plusmn;0.80 to \u0026plusmn;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003e\u0026nbsp;Very strong correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"6. Experiences and Challenges in AI Literacy and Situated Academic Writing","content":"\u003cp\u003eAnalysis of the open-ended interview responses revealed several key challenges that contribute to students\u0026rsquo; difficulties with AI literacy and academic writing. A commonly reported issue was limited exposure to AI concepts, with respondents noting unfamiliarity with terms such as \u003cem\u003emachine learning\u003c/em\u003e, \u003cem\u003ebig data\u003c/em\u003e, and \u003cem\u003eneural networks\u003c/em\u003e. This lack of foundational understanding reflects gaps in technological knowledge and helps explain lower scores in the Technological Understanding domain. Some participants also expressed misconceptions about how AI works, indicating uncertainty in distinguishing between its mechanisms and applications.\u003c/p\u003e\u003cp\u003eAnother challenge centered on academic writing skills, particularly difficulties with structuring essays and using formal vocabulary. Several students reported nervousness and a lack of confidence when tasked with longer writing assignments, pointing to persistent struggles in applying academic conventions. Respondents also described challenges in selecting and appropriately using scholarly terms, which often led to hesitation and self-doubt in their written work. These issues align with the relatively lower performance observed in Writing Essentials.\u003c/p\u003e\u003cp\u003eDespite these challenges, the analysis also uncovered promising opportunities. Many students expressed curiosity and a strong desire to deepen their knowledge of AI, with several indicating interest in learning both the technical and ethical aspects of AI use. This openness suggests a willingness to engage with AI in responsible and effective ways. Respondents also recognized the potential of AI tools to support their academic writing, provided they gain proper understanding of how such tools function. Furthermore, the expressed motivation to improve writing structure and vocabulary points to a readiness to benefit from the specific interventions, such as structured writing workshops and guided AI literacy programs.\u003c/p\u003e\u003cp\u003eOverall, the findings reveal that while students face significant challenges in both AI literacy and academic writing, their eagerness to learn and improve offers valuable opportunities for skill development. With appropriate support, these students may not only strengthen their writing proficiency but also cultivate a more comprehensive and responsible engagement with AI technologies.\u003c/p\u003e"},{"header":"7. Inputs to Writing Intervention Program","content":"\u003cp\u003eBased on the results of the study, the intervention program \u003cem\u003eWritIntel: Strengthening Writing Essentials and AI Technological Understanding among English Major Students\u003c/em\u003e was designed. The findings revealed that among the dimensions of AI literacy, Technological Understanding obtained the lowest composite mean (M\u0026thinsp;=\u0026thinsp;3.16, SD\u0026thinsp;=\u0026thinsp;0.67, Moderate), suggesting that while students show awareness of AI, they lack deeper comprehension of how AI systems function\u0026mdash;particularly in relation to neural networks, supervised learning, and machine learning. Similarly, in situated academic writing, Writing Essentials recorded the lowest scores (M\u0026thinsp;=\u0026thinsp;3.59, 64% proficiency), indicating difficulties in synthesizing sources, using academic vocabulary, and overcoming writing challenges. These results provide the basis for a structured program that integrates academic writing instruction with AI concept enhancement.\u003c/p\u003e\u003cp\u003eThe content of the program is organized into five core modules. Module 1 introduces AI fundamentals through interactive lectures on neural networks, reinforcement learning, and decision-making systems. Module 2 focuses on writing essentials through hands-on workshops in vocabulary use, paraphrasing, synthesis, and structuring academic arguments. Module 3 links both strands by assigning integrated writing tasks where students explain AI concepts in their own words. Module 4 introduces students to AI-supported writing tools such as Grammarly, Quillbot, Gemini, and ChatGPT, with attention to ethical use and limitations. Finally, Module 5 incorporates peer and instructor feedback, enabling students to revise drafts and reflect on their learning through journals. Together, these modules are designed to strengthen technical understanding while enhancing foundational writing skills.\u003c/p\u003e\u003cp\u003eThe program is set for a duration of eight weeks, with two one-hour sessions per week. The participants include English major students at Romblon State University who demonstrated moderate AI literacy in Technological Understanding and lower proficiency in Writing Essentials. To support engagement, the program combines lectures, practice-based writing activities, and peer collaboration. Progress will be monitored through weekly attendance, task submissions, and mid-program feedback surveys, ensuring that participants remain on track toward the objectives. Importantly, \u003cem\u003eWritIntel\u003c/em\u003e is designed as a sustainable initiative. It will be implemented yearly for all incoming first-year students and will continue until their third year, allowing results to be tracked and monitored annually to assess long-term effectiveness.\u003c/p\u003e\u003cp\u003eEvaluation of the program will adopt both quantitative and qualitative approaches. Pre- and post-tests will measure gains in AI knowledge, with an expected 15% increase as an indicator of improvement. Writing performance will be assessed through rubric-based scoring, with a target of at least one proficiency level increase. Reflection journals, exit surveys, and a possible focus group discussion will provide further insights into students\u0026rsquo; experiences and confidence levels. By combining structured instruction, practical activities, continuous feedback, and a sustainable yearly implementation plan, the \u003cem\u003eWritIntel\u003c/em\u003e program aims not only to address current gaps but also to ensure lasting improvement in AI literacy and academic writing skills across cohorts of English major students.\u003c/p\u003e"},{"header":"8. Discussion","content":"\u003cp\u003eThis study provides important insights into the relationship between Artificial Intelligence (AI) literacy and situated academic writing among English major students at Romblon State University, revealing both areas of strength and dimensions requiring further development. While students demonstrated confidence in ethical and applied aspects of AI, as well as reflective and creative domains of writing, the results also identified persistent challenges in technical understanding and foundational writing skills. The findings suggest that students are aware of the role of AI and writing in academic contexts, yet still face barriers in technical comprehension and mechanics. This gap reflects a pattern also observed in prior research: positive attitudes and awareness that are not always matched by deeper conceptual mastery or writing competence.\u003c/p\u003e\u003cp\u003eOne of the most notable findings concerns students\u0026rsquo; low scores in Technological Understanding (M\u0026thinsp;=\u0026thinsp;3.16, SD\u0026thinsp;=\u0026thinsp;0.67), which indicates limited grasp of AI concepts such as neural networks, supervised learning, and machine learning. This is consistent with Laupichler et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and Juma [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], who reported similar deficiencies in students\u0026rsquo; technical comprehension despite widespread awareness of AI. In contrast, students scored highly in Critical Appraisal (M\u0026thinsp;=\u0026thinsp;3.69) and Practical Application (M\u0026thinsp;=\u0026thinsp;3.60), demonstrating awareness of data privacy, ethical responsibility, and the use of AI tools in academic and real-world settings. This finding echoes Wang et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], who likewise observed strong ethical literacy but conceptual gaps in AI knowledge. These results indicate that students may be engaging with AI tools pragmatically without fully understanding the underlying systems, reinforcing the need for experiential instruction that bridges practice and theory.\u003c/p\u003e\u003cp\u003eIn terms of situated academic writing, students rated themselves moderately proficient in Writing Essentials (M\u0026thinsp;=\u0026thinsp;3.59), while showing higher self-confidence in Relational-Reflective (M\u0026thinsp;=\u0026thinsp;3.98) and Creative-Identity (M\u0026thinsp;=\u0026thinsp;3.74). Inter-rater evaluations supported this trend, rating students as \u0026ldquo;Proficient\u0026rdquo; across all domains, with the highest scores in Relational-Reflective (M\u0026thinsp;=\u0026thinsp;11.13, 74%) and lower performance in Writing Essentials (M\u0026thinsp;=\u0026thinsp;9.56, 64%). This convergence suggests that students\u0026rsquo; self-awareness of their writing strengths and weaknesses is generally reliable. However, slight discrepancies emerged: students tended to overestimate their reflective abilities, while underestimating their foundational skills. This aligns with Sehlstr\u0026ouml;m et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], who noted that high self-efficacy does not always equate to higher performance, and Jalaluddin et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who found that students with lower confidence sometimes achieve stronger outcomes. These findings explain the complexity of self-efficacy in writing and the need for structured feedback to calibrate students\u0026rsquo; self-perceptions.\u003c/p\u003e\u003cp\u003eThe correlation analysis further revealed a moderate to strong positive relationship between AI literacy and situated academic writing, particularly between Technological Understanding and Writing Essentials (r\u0026thinsp;=\u0026thinsp;0.61), as well as between Creative-Identity and both reflective and foundational domains. This suggests that students who are more confident in their AI comprehension tend also to show stronger writing performance, especially in tasks requiring synthesis and originality. These results are in line with Bekturova et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], who argue that digital competencies can enhance confidence and academic performance. However, the qualitative findings revealed challenges that temper these positive associations. Students expressed difficulty with AI terminology, nervousness in using academic vocabulary, and uncertainty in structuring essays. This highlights a potential disconnect: while correlations suggest a reinforcing relationship, actual learning experiences expose continuing struggles that need specific intervention.\u003c/p\u003e\u003cp\u003eDespite these challenges, the study also uncovered significant opportunities. Students expressed eagerness to improve their AI knowledge and writing competence, with many acknowledging that stronger literacy in both areas would benefit their academic growth. This openness reflects findings by Wood et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. At the same time, the sustainability of these efforts must be ensured through structured programs, such as the proposed \u003cem\u003eWritIntel\u003c/em\u003e intervention, which integrates AI literacy with writing instruction and is designed for yearly implementation. By systematically addressing gaps in technological understanding and writing essentials while reinforcing reflective and creative strengths, such interventions can provide long-term benefits for multiple cohorts of English major students.\u003c/p\u003e\u003cp\u003eFinally, situating these findings in broader contexts reveals that the challenges observed are not unique to Romblon State University. Similar patterns of strong ethical awareness but weak technical understanding have been documented in both local [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and international [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] contexts. Likewise, struggles in foundational writing skills amid confidence in reflective and creative expression have been noted by [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This regional and global consistency proves the need for educational strategies that integrate technical, ethical, and practical dimensions of AI literacy with socially situated approaches to academic writing. Cross-institutional collaboration, curriculum integration, and sustainable interventions can help bridge these gaps, ensuring that students not only engage with AI tools and writing practices but also master the underlying knowledge and skills necessary for advanced academic and professional competence.\u003c/p\u003e"},{"header":"9. Limitations","content":"\u003cp\u003eWhile this study makes valuable contributions to understanding the relationship between AI literacy and situated academic writing, several limitations must be acknowledged. First, the reliance on self-reported perceptions for AI literacy and writing self-efficacy raises the possibility of social desirability bias, where students may have overestimated their competence or downplayed difficulties to align with perceived academic expectations. This reliance limits the precision of the findings, as students\u0026rsquo; actual comprehension and performance may not fully match their reported confidence.\u003c/p\u003e\u003cp\u003eSecond, the study was conducted with English major students from a single institution, which narrows the generalizability of the results to broader student populations. Since AI exposure and writing practices can vary across disciplines, universities, and regional contexts, further research in diverse academic and institutional settings is needed to validate and extend these findings.\u003c/p\u003e\u003cp\u003eThird, while inter-rater evaluations were used to assess situated academic writing proficiency, the study did not incorporate direct performance-based assessments of AI literacy, such as task-based evaluations or practical demonstrations of AI tool usage. Including such measures in future work would provide a more objective account of students\u0026rsquo; abilities and complement the self-assessment data.\u003c/p\u003e\u003cp\u003eFuture research should adopt mixed-methods approaches, integrating surveys with classroom observations, writing portfolio analyses, and task-based AI literacy assessments to build a more comprehensive understanding. Longitudinal studies are also recommended to examine how students\u0026rsquo; AI literacy and writing proficiency evolve across their undergraduate years, especially as programs like \u003cem\u003eWritIntel\u003c/em\u003e are sustained and implemented annually. Such designs would capture both immediate gains and long-term developmental trajectories, offering stronger evidence for the effectiveness of integrated interventions.\u003c/p\u003e"},{"header":"10. Conclusions","content":"\u003cp\u003eThis study highlights both the potential of strengthening artificial intelligence (AI) literacy and situated academic writing among English major students at Romblon State University, as well as the challenges that remain in bridging technical knowledge and foundational writing skills. While students demonstrated strong competence in critical appraisal and practical application\u0026mdash;particularly in understanding ethical implications, data privacy, and the relevance of AI in daily life\u0026mdash;their technological understanding of core concepts such as machine learning and neural networks was only moderate. In parallel, students reported confidence in relational-reflective and creative-identity writing, showing preparedness in audience awareness, originality, and the use of feedback, yet continued to face difficulties in writing essentials such as source synthesis and academic vocabulary. Inter-rater evaluations confirmed overall proficiency across writing domains, though foundational skills still require focused improvement. These findings also revealed moderate to strong correlations between AI literacy and writing performance, indicating that greater digital literacy, especially in technical and ethical areas, supports stronger academic writing competence.\u003c/p\u003e\u003cp\u003eImportantly, the results suggest that enhancing AI literacy and academic writing should be pursued as a sustained process rather than a one-time intervention. The proposed \u003cem\u003eWritIntel\u003c/em\u003e program responds directly to these needs, integrating instruction in technological understanding and writing essentials through a structured, multi-component design. As a sustainable initiative, the program will be implemented annually for first-year students and monitored through their undergraduate years, allowing progress to be tracked up to the third year. By aligning AI knowledge with academic writing practice, the program provides a long-term pathway for students to advance from proficiency to higher competence levels, ensuring they are equipped to meet the demands of academic coursework and to thrive in digital and knowledge-driven environments.\u003c/p\u003e"},{"header":"11. Recommendations","content":"\u003cp\u003eThe researchers recommend the following:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eCapacity building in AI literacy and writing competence\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eStrengthening students\u0026rsquo; academic performance requires structured programs that integrate AI literacy with foundational writing skills. Institutions should design hands-on, practice-based workshops focused on technological understanding of AI concepts alongside training in writing essentials such as source synthesis, academic vocabulary, and argument organization. Regular assessments and writing clinics should be implemented to track growth, while peer-learning groups and writing circles can provide sustained collaborative support.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eSustainable program implementation and resource support\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo ensure long-term impact, the WritIntel program should be institutionalized as a yearly intervention for all first-year English major students, with continuous monitoring until their third year. Adequate resources\u0026mdash;such as access to AI tools, writing-enhancement software, and updated instructional materials\u0026mdash;must be provided to support program activities. Faculty should also be trained to integrate AI content into coursework and to guide students in responsible, ethical use of AI in academic writing.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eInstitutional alignment and collaborative partnerships\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor effectiveness and sustainability, the intervention program should be fully aligned with Romblon State University\u0026rsquo;s educational policies and curriculum frameworks. Establishing partnerships with AI practitioners, professional organizations, and educational agencies will strengthen technical and instructional resources. Collaborative efforts will also enable broader dissemination of best practices, equitable resource allocation, and the scaling of successful strategies across departments and related programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Romblon State University-College of Education (RSU-CED) for hosting the authors during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe task distribution for the final manuscript development is as follows: RBG and AMG contributed to the writing of Introduction and Literature Review; CS contributed to the writing of Methodology; CH and SA contributed to the writing of Findings and Conclusion; MS contributed to the writing of Discussion; Formatting and Reference Management were done by RBG and AMG; and Proofreading and Final Editing of the manuscript were done by MS. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause of reasons of sensitivity and protection of anonymity, the data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval for its general characteristics from the Romblon State University - College of Education. This ensured that careful consideration of research ethics principles, potential risks to participants, informed consent procedures, and data protection measures was made prior to the conduct of the study in conformity with the standard ethical guidelines. The authors confirm that informed consent was obtained from all participants prior to their participation in the study to ensure their voluntary participation. No participants under the age of 16 were involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkkaya, A. \u0026amp; Aydin, G. (2018). Academics\u0026apos; Views on the Characteristics of Academic Writing. 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Are we ready to integrate artificial intelligence literacy into medical school curriculum: Students and faculty survey. \u003cem\u003eJournal of Medical Education and Curricular Development, 8. \u003c/em\u003ehttps://doi.org/10.1177/238212. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AI literacy, English major students, higher education, situated academic writing","lastPublishedDoi":"10.21203/rs.3.rs-7644540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7644540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of artificial intelligence (AI) into higher education is increasingly recognized as essential for developing future-ready academic skills. While research on AI literacy is expanding globally, studies connecting AI knowledge with situated academic writing in the Philippine context remain limited. This study examines the AI literacy and academic writing profiles of 54 first-year English major students at Romblon State University. A mixed-method design was employed, utilizing survey questionnaires, and semi-structured interviews. Results indicate that students reported high literacy in critical appraisal (M\u0026thinsp;=\u0026thinsp;3.74, SD\u0026thinsp;=\u0026thinsp;0.65) and practical application (M\u0026thinsp;=\u0026thinsp;3.59, SD\u0026thinsp;=\u0026thinsp;0.71), but only moderate literacy in technological understanding (M\u0026thinsp;=\u0026thinsp;3.16, SD\u0026thinsp;=\u0026thinsp;0.68). Inter-rater evaluations of situated academic writing showed overall proficiency (M\u0026thinsp;=\u0026thinsp;10.31, SD\u0026thinsp;=\u0026thinsp;1.80, 68.67%), with relational-reflective (M\u0026thinsp;=\u0026thinsp;11.13, SD\u0026thinsp;=\u0026thinsp;1.72, 74%) as the highest-performing domain and writing-essentials (M\u0026thinsp;=\u0026thinsp;9.56, SD\u0026thinsp;=\u0026thinsp;1.93, 64%) as the lowest. Correlation analyses revealed positive and statistically significant associations between AI literacy and writing domains, with the strongest relationships found between creative-identity and writing-essentials (r\u0026thinsp;=\u0026thinsp;0.77) and between critical appraisal and practical application (r\u0026thinsp;=\u0026thinsp;0.84). Qualitative analysis of interview responses further revealed three major themes: gaps in understanding of AI terminology, strong curiosity to learn more about AI, and challenges in using formal academic language. The findings suggest that while students demonstrate reflective and creative writing competence alongside awareness of AI ethics and applications, they continue to face challenges in technical AI concepts and foundational writing skills. The study proposes the \u003cem\u003eWritIntel\u003c/em\u003e program, an annual intervention to be implemented for three years, for continuous longitudinal monitoring and long-term development of AI literacy and academic writing proficiency.\u003c/p\u003e","manuscriptTitle":"Exploring Artificial Intelligence (AI) Literacy and Situated Academic Writing of Students in a Philippine State University: Inputs to Writing Intervention Program","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 16:28:56","doi":"10.21203/rs.3.rs-7644540/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T11:38:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-17T07:26:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T02:59:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2025-10-10T02:55:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c31c1da-a0b8-4134-8f57-29d483b8b374","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T21:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 16:28:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7644540","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7644540","identity":"rs-7644540","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}