Generative AI Integration in Higher Education Teaching: Reflections and Experiences Based on Technological Pedagogical Content Knowledge (TPACK) Model

Generative AI Integration in Higher Education Teaching: Reflections and Experiences Based on Technological Pedagogical Content Knowledge (TPACK) Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Generative AI Integration in Higher Education Teaching: Reflections and Experiences Based on Technological Pedagogical Content Knowledge (TPACK) Model Louie Giray, John Christopher Castillo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8921329/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This study investigated how college faculty integrate generative AI (GenAI) tools into their teaching practices, focusing on their reflections through the Technological Pedagogical Content Knowledge (TPACK) lens. Results revealed that GenAI integration transforms all three TPACK domains. In content knowledge, teachers emphasized the need to critically assess AI-generated material, signaling a shift toward AI-informed disciplinary expertise. In pedagogical knowledge, participants used GenAI to personalize instruction, enhance assessments, and create new forms of student engagement, while maintaining a strong emphasis on human connection and academic rigor. In technological knowledge, faculty demonstrated adaptive use of GenAI, distinguishing between AI's generative capabilities and the pedagogical judgment needed to guide them ethically and effectively. Ethical concerns surfaced around dependency, authenticity, and integrity. This underscores the need for thoughtful integration that aligns technological possibilities with pedagogical intent and content accuracy. Overall, successful GenAI use occurred when teachers actively blended all three knowledge areas, not when relying on technology alone. These results affirm the evolving relevance of the TPACK model and highlight the need for ongoing, values-driven faculty development. academic integrity AI literacy pedagogy TPACK 1 INTRODUCTION The rapid evolution of generative artificial intelligence (GenAI) has prompted substantial shifts in higher education worldwide. Emerging evidence demonstrates that tools like ChatGPT are influencing teaching, learning, and assessment practices by automating knowledge generation and transforming interactions between instructors and students [ 1 ]. While GenAI offers significant opportunities to personalize learning and streamline instructional processes [ 2 , 3 ], it simultaneously raises ethical concerns about academic integrity, bias, and overreliance on algorithmic outputs [ 4 , 5 ]. Amid these developments, faculty readiness and pedagogical competence have become critical factors in determining how effectively GenAI is integrated into higher education contexts [ 6 ]. Parallel to these technological advancements, the Technological Pedagogical Content Knowledge (TPACK) framework has gained prominence as a theoretical model describing the complex knowledge required for effective technology integration in teaching [ 7 , 8 ]. TPACK emphasizes the interplay among technological, pedagogical, and content knowledge, underscoring that sustainable innovation depends not only on tool availability but also on teachers' capacity to blend these domains meaningfully [ 9 ]. Despite growing research on GenAI's potential and challenges, limited studies have systematically explored how college teachers reflect on and conceptualize GenAI adoption through the lens of TPACK. Prior investigations have focused primarily on policy guidelines [ 10 , 11 ], student perspectives [ 12 , 13 ], or broad institutional strategies [ 14 ], often overlooking the nuanced ways educators perceive their technological, pedagogical, and content knowledge evolving in response to GenAI. This gap restricts understanding of how teachers situate GenAI within their professional practices and how these reflections inform implementation decisions. To address this gap, the present study explores college teachers' reflections and experiences on generative AI integration using the TPACK framework as an analytical lens. This research aims to illuminate how educators negotiate the intersection of AI and pedagogical responsibilities. The findings seek to contribute to strategies that support responsible, context-sensitive adoption of GenAI in higher education. 2 REVIEW OF RELATED LITERATURE 2.1 Generative AI in Higher Education Recent scholarship has documented the transformative yet contested role of generative artificial intelligence (GenAI) in higher education. O'Dea [ 14 ] described GenAI as a paradigm shift, offering unprecedented support in knowledge creation while challenging established pedagogical norms. Francis et al. [ 2 ] underscored GenAI's duality: it promotes personalized learning and efficient assessment design but risks undermining academic integrity, exacerbating inequalities, and blurring authorship boundaries. Xia et al. [ 3 ] noted that GenAI compels rethinking assessment, advocating for authentic tasks, self-regulated learning, and transparency to uphold fairness. Institutions have responded variably. McDonald et al. [ 11 ] analyzed policies across U.S. universities, finding that while a majority encourage GenAI use, guidance often remains fragmented and burdensome for faculty. Wang et al. [ 15 ] reported that universities increasingly issue sample syllabi and ethics protocols, reflecting a shift from prohibition to conditional integration. Batista et al. [ 16 ] and Bobula [ 17 ] emphasized the need for comprehensive frameworks addressing data privacy, bias, and plagiarism detection. Yusuf et al. [ 5 ] further highlighted the cultural variability in perceptions of GenAI's benefits and risks, suggesting that implementation must be context-sensitive to diverse educational traditions and norms. Despite concerns, GenAI is increasingly integrated into curriculum design, research assistance, and student support services [ 1 ]. Lee et al. [ 20 ] observed that faculty attitudes are evolving: while some educators express apprehension over workload and ethical dilemmas, many recognize GenAI's inevitability in shaping future skills and institutional competitiveness. The literature converges on the notion that higher education is negotiating a delicate balance—leveraging GenAI's pedagogical potential while preserving academic standards and equity. Sustained professional development, transparent policies, and collaborative dialogue among stakeholders remain critical for responsible adoption and meaningful innovation. 2.2 College Teachers and Generative AI Faculty perspectives on generative artificial intelligence (GenAI) reflect ambivalence, shaped by perceived usefulness, self-efficacy, and institutional readiness. Shata and Hartley [ 18 ] reported that perceived usefulness strongly predicts faculty willingness to adopt GenAI, more so than ease of use or technological familiarity. Ellis et al. [ 13 ] documented qualitatively diverse experiences: some instructors view GenAI as a catalyst for creative pedagogy, while others fear dependency, student passivity, and erosion of critical thinking. Khlaif et al. [ 6 ] found that performance expectancy, social influence, and facilitating conditions significantly influenced instructors' behavioral intentions to integrate GenAI tools. Ruediger et al. [ 21 ] revealed widespread experimentation among faculty, though many lack clear strategies for aligning GenAI with learning objectives. Cacho [ 22 ] proposed balanced guidelines to support faculty in navigating ethical dilemmas, assessment integrity, and student engagement. Teachers have also voiced concerns over increased workload, uncertain institutional expectations, and the challenge of developing new competencies rapidly [ 2 , 17 ]. Giray et al. [ 23 ] further highlighted that reliance on AI writing detection tools can foster a culture of mistrust and punitive surveillance in higher education [ 24 ]. Their findings emphasize that educators often feel conflicted about using detection technologies, fearing unintended biases against multilingual students and negative impacts on academic relationships. Nonetheless, studies by Baytas and Ruediger [ 1 ] and Wang et al. [ 15 ] emphasize that targeted training, accessible resources, and clear policy frameworks can alleviate apprehensions and foster confidence in using GenAI. Lee et al. [ 20 ] further stressed that faculty require sustained support, as many feel underprepared to translate GenAI's capabilities into meaningful learning experiences. Overall, educators' reflections demonstrate that while GenAI's potential is widely recognized, its sustainable adoption depends on aligning technological tools with pedagogical goals, ethical standards, and discipline-specific content knowledge. Teachers occupy a pivotal role in translating these innovations into responsible, student-centered practices. 3 METHODS We employed a qualitative research design to investigate the reflections and experiences of faculty members in higher education. This approach allowed for rich, contextual insights into the GenAI tools in academic practice. Data were collected through an online, open-ended survey grounded in the Technological Pedagogical Content Knowledge (TPACK) framework [ 7 ]. The instrument was validated by two experts: one in AI in education and another in educational psychology. In June 2025, twelve faculty members from private universities in Manila, Philippines, participated in the study. The survey explored their experiences and reflections on using GenAI tools in teaching and academic work. Our participant pool included nine male and three female college teachers, representing a diverse range of academic disciplines (e.g., business, communication, psychology, social work, industrial engineering, literature, and physical education) and career stages within the private higher education sector. Educational backgrounds revealed a highly credentialed group: five participants held master's degrees and seven held doctoral degrees. Their teaching experience ranged from 1 to 21 years, with an average of 12.5 years. All participants reported using ChatGPT as their primary GenAI tool. Additional tools mentioned included Gemini, Claude, CoPilot, Grammarly, QuillBot, and Turnitin AI. Faculty members had acquired their knowledge of these tools through self-experimentation, YouTube tutorials, institutional training seminars, and informal peer collaboration. There were 12 participants, which aligns with the typical standard in qualitative research, where data saturation is often reached with 12 to 13 participants [ 25 ]. Meanwhile, data were analyzed using Braun and Clarke's [ 26 ] six-phase thematic analysis framework, which is particularly suited for exploratory qualitative research. This involved familiarization with the data, initial coding, theme development, theme review, theme definition, and report writing. Also, we adhered to ethical principles governing research involving human participants. Informed consent was obtained from all participants, with clear communication regarding the study's purpose, voluntary participation, and their right to withdraw at any time. Privacy and confidentiality were protected through data anonymization, secure data storage, and full compliance with relevant data privacy regulations. This study also has several limitations. The sample size was small and restricted to faculty members from private universities in a specific urban region, which may limit the generalizability of findings. Moreover, while the open-ended survey format allowed for flexible and reflective responses, it limited opportunities for follow-up probing. However, as an exploratory study, the intention was not to generalize but to gain deep insights into the early-stage experiences and reflections of faculty in this rapidly evolving area. 4 RESULTS Through our thematic analysis, we identified five key themes reflecting college teachers' experiences and reflections on GenAI integration in their teaching practices, revealing both the opportunities and challenges they encounter in this evolving educational landscape. The following themes are pragmatic adoption amid ethical tension, pedagogical transformation, vigilance on academic integrity, balancing human connection with GenAI enhancement, and strategic adaptation. Table 1 Summary of Themes Theme Definition Sample Statement Pragmatic Adoption Amid Ethical Tensions Faculty embrace GenAI for efficiency while maintaining ethical boundaries and professional integrity "When the task is rather repetitive or when I am pressed for time... [However, there are] ethical considerations. I also don't want to be labeled as a professor who is dependent on AI tools." - Cris Pedagogical Transformation GenAI catalyzes innovative teaching approaches, enhancing creativity in lesson design and content delivery "It made me create more engaging lessons and gave me more time to focus on getting to know the needs of my students better." - July Vigilance on Academic Integrity Maintaining rigorous content verification and addressing student over-dependence on AI-generated work "Review, validate, and revalidate. Conscious effort in keeping the highest level of academic integrity when it comes to generated information and data is imperative." - Peter Balancing Human Connection with GenAI Enhancement Preserving essential human elements in education while leveraging GenAI's supportive capabilities "GenAI is something that we should be prepared [for]. But it cannot replace genuine human connection, most especially in intervening with complex problems." - Peter Strategic Adaptation Dynamic evolution of teaching strategies in response to GenAI capabilities and ongoing professional development "[We must be] knowledgeable with how AI works to effectively gauge the quality of student[s'] output." - Martha 4.1 Theme 1: Pragmatic Adoption Amid Ethical Tensions College teachers demonstrated a pragmatic approach to GenAI adoption. They embraced these tools for efficiency while simultaneously grappling with significant ethical considerations. This theme reflects the ongoing tension between practical benefits and professional integrity concerns that permeates their decision-making processes. Cris articulated this balance clearly, explaining his usage pattern: "when the task is rather repetitive or when I am pressed for time," while simultaneously expressing concern about "ethical considerations" and his reluctance to "be labeled as a professor who is dependent on AI tools." This pragmatic stance was further evidenced by Mark's approach to tool selection, where he carefully considered "whether the GenAI tool support[s] our course goals by enhancing critical thinking and personalized learning rather than bypassing student effort." Joseph's perspective on industrial applications revealed how disciplinary contexts shape adoption decisions: "I use GenAI for easier processing as practiced in the industry," showing alignment between educational practices and professional standards. Many college teachers consistently emphasized that GenAI should supplement rather than replace human input and judgment. Peter's reflection captured this sentiment effectively: "It helped but I never utilized it as a primary source of information," while Janice described ChatGPT as "just a supplement" for "research purposes for clarification only [like a book]." This pragmatic yet cautious approach characterized the overall adoption pattern across all college teachers. 4.2 Theme 2: Pedagogical Transformation GenAI tools have catalyzed significant pedagogical shifts among college teachers, with college teachers reporting enhanced creativity in lesson design and content delivery that altered their teaching approaches. July's experience exemplified this transformation, explaining how GenAI "made me create more engaging lessons and gave me more time to focus on getting to know the needs of my students better." The creative enhancement extended beyond lesson planning to assessment strategies, as Martha observed: "It makes me more creative at assessing my students' level of critical thinking." Her reflection reveals how GenAI challenges traditional evaluation methods. Mark's approach to content customization demonstrated this creativity in action: "AI allows me to customize content based on student profiles," indicating a move toward more personalized learning facilitation. College teachers reported using GenAI for diverse creative purposes, from generating analogies to creating varied content formats that cater to different learning styles. Mark explained how he uses "AI to produce materials in different formats such as text and images to cater to diverse learning needs of students," while Gerald observed that "context and content become more creative" through AI integration. The transformation also involved rethinking traditional assignments and assessment methods, with Mark noting how GenAI "did not replace what I teach but it reshaped how I teach," leading to more "student-centered and ethical" approaches through "recalibrating teaching materials through AI tools." 4.3 Theme 3: Vigilance on Academic Integrity A dominant concern across all college teachers was maintaining academic integrity while using GenAI tools. This encompasses both the challenges of preventing student misuse and the imperative for rigorous content validation. James articulated a common frustration experienced by many teachers: "I get frustrated especially when students are becoming more dependent on AI tools," particularly noting the tendency for students to rely on AI rather than developing their own critical thinking skills. The imperative for validation emerged as a critical practice among college teachers, who developed systematic approaches to ensure content accuracy and reliability. Peter emphasized the nature of this process: "review, validate, and revalidate." He continued, "Conscious effort in keeping the highest level of academic integrity when it comes to generated information and data is imperative." Mark's approach of treating "AI content not as a source but only as a draft" and ensuring verification through "cross-checking with official and legitimate information sources such as textbooks and peer-reviewed journals" exemplified this vigilance and established clear protocols for responsible AI use. College teachers demonstrated acute awareness of GenAI's limitations in providing reliable information, which informed their cautious approach to implementation. Peter noted that "Not all data generated by GenAI is reliable. Some data are misleading," while Mike observed that "[there is still] much content [that] is [incorrect]." The integrity concerns extended to student behavior, with Mark identifying "academic integrity" as his "main challenge," noting the "tendency for some students to solely rely on AI to completely write their course requirements." 4.4 Theme 4: Balancing Human Connection with AI Enhancement College teachers consistently emphasized the irreplaceable value of human elements in education, while acknowledging GenAI's supportive role, reflecting a nuanced understanding of technology's place in pedagogical practice. This theme captures the ongoing tension between technological advancement and preserving essential human aspects of teaching and learning. Peter's reflection on social work education particularly captured this balance: "GenAI is something that we should be prepared [for]. But it cannot replace genuine human connection, most especially in intervening with complex problems." The importance of human touch emerged as particularly crucial in disciplines involving complex human experiences and emotional intelligence. Peter noted that GenAI "lacked human touch and cannot discuss and comprehend complex human experiences which can only be captured by people who have experienced it firsthand." This recognition shaped how college teachers integrated GenAI while deliberately preserving essential human elements in their teaching practice. The limitation was particularly relevant in fields like social work, psychology, and education, where understanding human complexity requires experiential knowledge and emotional intelligence that AI currently cannot replicate. College teachers maintained their focus on developing personal connections with students despite technological integration. July emphasized "focusing on the needs of my students and how they can learn better in my class," while Mike stressed the importance of knowing students personally: "[You] need to know the children personally." This personal dimension remained central to their teaching philosophy despite technological integration, suggesting that effective education requires human understanding that transcends technological capabilities. Martha's concept of "practice wisdom" illustrated how human judgment remained essential in evaluating GenAI outputs: "You know it already when [it's] GenAI who made the analysis for them." This experiential knowledge represented an irreplaceable human element that college teachers valued alongside technological capabilities, demonstrating how professional expertise and intuition remain crucial in the AI-integrated classroom. 4.5 Theme 5: Strategic Adaptation College teachers demonstrated remarkable adaptive strategies in response to GenAI capabilities, continuously evolving their approaches based on experience and changing student needs. This theme reflects the dynamic nature of GenAI integration and college teachers' commitment to ongoing professional development in response to technological change. Erasmus's approach exemplified this adaptability: "Simple identification and take homework will no longer be enough as answers can just be generated [through] AI." The strategic adaptation involved developing innovative pedagogical approaches that leveraged AI capabilities while maintaining educational rigor. Mark's "triangular dialogue" approach, where students "suggest ideas, AI expands them, while I guide critical evaluation," demonstrated how teachers are creating new instructional strategies that emerge from GenAI integration rather than simply adopting existing tools. Meanwhile, college teachers showed strong commitment to continuous learning about GenAI capabilities and limitations. Mike's experience with training seminars on "how to properly use ChatGPT" and learning to avoid AI dependency reflected this learning orientation, while James's learning through "co-faculty and school webinars" indicated collaborative approaches to professional development. The strategic dimension extended to preparing students for AI-integrated futures, with Joseph focusing on helping students "decipher errors and fix them while coding" and Martha emphasizing the importance of being "knowledgeable with how AI works to effectively gauge the quality of student[s'] output." 5 DISCUSSION The integration of GenAI tools among college teachers reveals complex negotiations across the three foundational domains of the TPACK framework. We argue that successful GenAI adoption requires understanding of how these knowledge domains intersect and transform in AI-enhanced educational environments. 5.1 Content Knowledge (CK) in the GenAI Era Our findings reveal that GenAI integration challenges traditional conceptions of content knowledge in higher education. The vigilance on academic integrity demonstrated by participants reflects what we interpret as a profound renegotiation of disciplinary expertise and authority. When Peter emphasizes the need to "review, validate, and revalidate" AI-generated content, we observe that college teachers need to understand not just disciplinary content but also how to evaluate AI-generated information within specific academic domains. This aligns with Wineburg's [ 27 ] work on historical thinking, which emphasizes the importance of source evaluation and critical analysis skills that become even more crucial in AI-mediated information environments. Peter's observation about education that GenAI "cannot replace genuine human connection" and "lacked human touch" demonstrates domain-specific content knowledge that recognizes the irreplaceable value of experiential and affective understanding. We believe this reflects what Shulman [ 28 ] conceptualized as the transformation of content knowledge through pedagogical reasoning. Now, this extended to include AI-mediated content evaluation [ 10 ]. The strategic adaptation theme reveals how content knowledge evolves to include AI literacy as a fundamental component. Joseph's focus on helping students "decipher errors and fix them while coding" exemplifies how disciplinary content knowledge now necessarily includes understanding AI capabilities and limitations. This transformation echoes Prensky's [ 29 ] observations about digital literacy evolution, though AI integration presents more complex challenges requiring what Selwyn [ 30 ] describes as critical technological literacy that goes beyond operational skills to encompass understanding of technological limitations and biases. Again, we put forward that content knowledge in higher education must expand beyond traditional disciplinary boundaries to encompass critical AI evaluation skills that are domain-specific yet technologically informed. 5.2 Pedagogical Knowledge (PK) Transformation The pedagogical transformation documented in our themes represents a reconceptualization of teaching knowledge in AI-enhanced environments. July's experience of using GenAI to create "more engaging lessons" while gaining "more time to focus on getting to know the needs of my students better" demonstrates what we interpret as the evolution of pedagogical knowledge toward more personalized, responsive teaching approaches. This aligns with constructivist pedagogical principles [ 31 ] while leveraging AI's capabilities to enhance rather than replace human pedagogical judgment. Mark's development of the "triangular dialogue" approach, where "students suggest ideas, AI expands them, while I guide critical evaluation" exemplifies sophisticated pedagogical knowledge that integrates AI capabilities into established collaborative learning frameworks. This approach resonates with Laurillard's [ 32 ] conversational framework for learning, which emphasizes the importance of dialogue and reflection in educational processes, now extended to include AI as a conversational partner under human guidance. We believe that college teachers must understand how to orchestrate learning experiences that leverage AI tools while maintaining pedagogical intentionality and human guidance. The emphasis on human connection revealed in our findings suggests that pedagogical knowledge in the AI era must encompass understanding of when and how to preserve essential human elements in teaching. This finding reinforces Noddings' [ 33 ] emphasis on caring relationships in education. On the other hand, the creative enhancement in assessment strategies demonstrates pedagogical knowledge evolution toward a more elevated kind of evaluation approaches. Martha's observation that GenAI "makes me more creative at assessing my students' level of critical thinking" suggests that pedagogical knowledge must now include understanding how AI tools can enhance rather than compromise educational assessment, but academic rigor must be maintained, still. This aligns with Black and Wiliam's [ 34 ] assessment principles, which emphasize the importance of feedback and adaptation in learning processes, now potentially enhanced through AI-supported assessment differentiation and personalization. 5.3 Technological Knowledge (TK) in Educational Context The technological knowledge demonstrated by participants extends far beyond basic tool proficiency to encompass sophisticated understanding of AI capabilities, limitations, and appropriate educational applications. The pragmatic adoption patterns revealed in our findings suggest that effective technological knowledge in education requires the ability to evaluate technological, especially AI, tools based on educational rather than purely technical criteria. Mark's approach of treating "AI content not as a source but only as a draft" demonstrates technological knowledge that encompasses understanding AI's role in educational workflows rather than simply its operational features. This approach to tool integration reflects what Mishra and Koehler [ 7 ] describe as the contextual nature of technological knowledge in educational settings, where technical capabilities must be understood in relation to pedagogical goals and content requirements. The commitment to continuous learning demonstrated by participants reflects recognition that technological knowledge in rapidly evolving AI contexts requires ongoing professional development. This resonates with Fullan's [ 35 ] emphasis on change knowledge and continuous learning as essential components of educational innovation, though AI integration presents unique challenges requiring what Zhao [ 36 ] describes as technology fluency that goes beyond basic operational skills to encompass understanding of technological affordances and constraints in educational contexts. Meanwhile, the concern about student dependency on AI tools demonstrates technological knowledge that encompasses understanding of appropriate technology use in educational contexts. This suggests that technological knowledge for college teachers must include not only how to use AI tools effectively but also how to teach students appropriate and ethical AI utilization practices. By and large, we find that successful GenAI integration occurs at the intersections of content, pedagogy, and technology, not in isolation. To teach effectively in the age of AI, college teachers must contextually blend these domains. Ethical tensions emerged as participants navigated the balance between efficiency and academic integrity, showing that thoughtful integration requires aligning technological capabilities with pedagogical values and content accuracy. These findings affirm the continued relevance of the TPACK framework, though each domain is evolving in response to AI. 6 CONCLUSION This study has highlighted how faculty in higher education are navigating the integration of generative AI tools within their teaching practice through the evolving dimensions of the TPACK framework. The results show that successful GenAI use requires more than tool proficiency; it demands a deep rethinking of content, pedagogy, and technology as interdependent domains. Faculty members actively adapted their instructional methods, assessments, and disciplinary thinking, treating GenAI as a collaborator rather than a replacement. At the same time, ethical concerns around academic integrity, student overreliance, and the erosion of human connection were repeatedly emphasized. These tensions underscore the need for intentional, critical integration that preserves pedagogical values while embracing innovation. Faculty reflections point toward an emergent model of AI-enhanced teaching that is dynamic, human-centered, and rooted in disciplinary expertise. The TPACK framework remains relevant but must now accommodate the ethical and epistemological shifts that GenAI tools introduce to higher education contexts. We recommend sustained faculty development on GenAI ethics and literacy, interdisciplinary collaboration for AI-integrated pedagogy, and institutional policies that promote transparent, critical AI use. Future studies should explore longitudinal effects across contexts to support scalable, responsible innovation that upholds educational integrity and centers human judgment in AI-enhanced learning. Declarations Ethics approval The research was conducted with the knowledge and ethical approval of the corresponding author’s institution: Mapúa University dated May 20, 2025. All procedures involving human participants were performed in accordance with institutional guidelines and recognized ethical standards. Compliance with the Data Privacy Act of 2012 ensured lawful data handling. Participation was voluntary, and respondents were informed of their right to withdraw at any time. No identifying information was collected, and all data were stored in a password-protected database accessible only to the research team. These safeguards protected participants’ privacy, autonomy, and confidentiality. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish Not applicable. Clinical trial number Not applicable. Conflict of Interest We declare no conflict of interest. Employment There are no employment financial interest losses or gains to declare either current or previous for any of the authors. Funding We received no internal or external funding. Declaration of Competing Interest The authors declare no competing interests. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author upon reasonable request. Clinical trial number Not applicable. Conflict of Interest The authors declare no conflict of interest. Employment There are no employment financial interest losses or gains to declare either current or previous for any of the authors Funding The authors received no internal or external funding. Declaration of Competing Interest The authors declare no competing interests. Informed consent Participants were informed about the study’s procedures, risks, benefits, and other aspects before their participation. Only those who gave their consent were allowed to participate in the research. Authors’ contributions Louie led the conceptualization, writing, analysis, manuscript development, critical revisions, and overall direction of the study. John Christopher contributed significantly to the data gathering, literature review, analysis, and manuscript development. All authors read and approved the final manuscript. Disclosure statement The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Including financial and non-financial interests. Declaration of generative AI use ChatGPT 4.0 was used solely for proofreading and enhancing the language of this work. It did not contribute to the development of ideas, arguments, or analysis. The authors take full responsibility for the content and originality of the manuscript. References Baytas C, Ruediger D, Making. AI generative for higher education: adoption and challenges among instructors and researchers. New York: Ithaka S + R; 2025. Francis NJ, Jones S, Smith DP. Generative AI in higher education: balancing innovation and integrity. Br J Biomed Sci. 2025;81:14048. Xia Q, Weng X, Ouyang F, Lin TJ, Chiu TKF. A scoping review on how generative artificial intelligence transforms assessment in higher education. Int J Educ Technol High Educ. 2024;21:40. Giray L. ChatGPT references unveiled: distinguishing the reliable from the fake. Internet Ref Serv Q. 2024;28(1):9–18. Yusuf A, Pervin N, Román-González M. Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. Int J Educ Technol High Educ. 2024;21:21. Khlaif Z, Salha S, Salameh N. Higher education instructors' behavioural intention to use ChatGPT: a mixed-methods study. Educ Inf Technol. 2024;29(3):2233–54. Mishra P, Koehler MJ. Technological pedagogical content knowledge: a framework for teacher knowledge. Teach Coll Rec. 2006;108(6):1017–54. Schmidt DA, Baran E, Thompson AD, Mishra P, Koehler MJ, Shin TS. Technological pedagogical content knowledge (TPACK): the development and validation of an assessment instrument for preservice teachers. J Res Technol Educ. 2009;42(2):123–49. Jiménez Sierra ÁA, Ortega Iglesias JM, Cabero-Almenara J, Palacios-Rodríguez A. Development of the teacher's technological pedagogical content knowledge (TPACK) from the Lesson Study: a systematic review. Front Educ. 2023;8:1078913. Giray L, Sevnarayan K, Ranjbaran Madiseh F. Beyond policing: AI writing detection tools, trust, academic integrity, and their implications for college writing. Internet Ref Serv Q. 2025;29(1):83–116. McDonald N, Johri A, Ali A, Hingle Collier A. Generative artificial intelligence in higher education: evidence from an analysis of institutional policies and guidelines. Comput Hum Behav Artif Hum. 2025;3:100121. Giray L, Aquino R. Use and impact of ChatGPT on undergraduate engineering students: a case from the Philippines. Internet Ref Serv Q. 2024;28(4):453–62. Ellis R, Han F, Cook H. Qualitatively different teacher experiences of teaching with generative artificial intelligence. Int J Educ Technol High Educ. 2025;22:33. O'Dea X. Generative AI: is it a paradigm shift for higher education? Stud High Educ. 2024;49(5):811–6. Wang H, Dang A, Wu Z, Mac S. Generative AI in higher education: seeing ChatGPT through universities' policies, resources, and guidelines. Comput Educ Artif Intell. 2024;7:100326. Batista J, Mesquita A, Carnaz G. Generative AI and higher education: trends, challenges, and future directions from a systematic literature review. Information. 2024;15(11):676. Bobula M. Generative artificial intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications. J Learn Dev High Educ. 2024;30. Shata A, Hartley K. Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI. Int J Educ Technol High Educ. 2025;22:14. Ellis R, Han F, Cook H. Sustainability considerations for AI-supported teaching practices in higher education. Sustainability. 2025;17(4):14590. Lee D, Arnold M, Srivastava A, Plastow K, Strelan P, Ploeckl F, Lekkas D, Palmer E. The impact of generative AI on higher education learning and teaching: a study of educators' perspectives. Comput Educ Artif Intell. 2024;6:100221. Ruediger D, Blankstein M, Love S, Generative. AI and postsecondary instructional practices: findings from a national survey of instructors. New York: Ithaka S + R; 2024. Cacho RM. Integrating generative AI in university teaching and learning: a model for balanced guidelines. Online Learn. 2024;28(3):55–81. Giray L, De Silos PY, Adornado A, Buelo RJV, Galas E, Reyes-Chua E, Santiago C, Ulanday ML. Use and impact of artificial intelligence in Philippine higher education: reflections from instructors and administrators. Internet Ref Serv Q. 2025;28(3):315–38. Giray L. The problem with false positives: AI detection unfairly accuses scholars of AI plagiarism. Ser Libr. 2024:1–9. Laja P. What's a good sample size for qualitative research? Wynter [Internet]. 2025 Jan 4 [cited 2026 Feb 20]. Available from: https://wynter.com/post/a-good-sample-size-for-qualitative-research Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101. Wineburg S. Historical thinking and other unnatural acts: charting the future of teaching the past. Philadelphia (PA): Temple University; 2001. Shulman LS. Knowledge and teaching: foundations of the new reform. Harv Educ Rev. 1987;57(1):1–22. Prensky M. Digital natives, digital immigrants. Horizon. 2001;9(5):1–6. Selwyn N. Is technology good for education? Cambridge (UK): Polity; 2016. Vygotsky LS. Mind in society: the development of higher psychological processes. Cambridge (MA): Harvard University Press; 1978. Laurillard D. Teaching as a design science: building pedagogical patterns for learning and technology. New York (NY): Routledge; 2013. Noddings N. The challenge to care in schools: an alternative approach to education. 2nd ed. New York (NY): Teachers College; 2015. Black P, Wiliam D. Assessment and classroom learning. Assess Educ Princ Policy Pract. 1998;5(1):7–74. Fullan M. The new meaning of educational change. 4th ed. New York (NY): Teachers College; 2016. Zhao Y. What teachers need to know about technology? Framing the question. In: Zhao Y, editor. What should teachers know about technology: perspectives and practices. Greenwich (CT): Information Age Publishing; 2003. pp. 1–14. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers invited by journal 03 Apr, 2026 Editor assigned by journal 19 Feb, 2026 Submission checks completed at journal 19 Feb, 2026 First submitted to journal 19 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8921329","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617805186,"identity":"7ac1a7a1-fa6f-456c-a0b8-1a216360d666","order_by":0,"name":"Louie Giray","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACPiA+AMRyMAHGBgYGNrxa2KBajEnTAgKJDcRr4V9+8XBFzeH0te1nj0n8YLCR3XCA99gDvFok3hQcPHPscO62M3lpkj0MacYbDvClG+DXcibhYAMbUMuBHDMJHobDiRsO8JhJENby73C62fk3ZpJ/GP4ToYW//cDBxrbDCWY3csykeRgOEGMLD8PBxr50w2033iVbyxgkG888zJeGVws///HHHxu+Wcubnc89ePNNhZ1s3/HeY3i1MEjkgIKnGYh5gBjEZubBqwFozfEHQLIOqgUMCGkZBaNgFIyCkQYA8CxQLqYXP1IAAAAASUVORK5CYII=","orcid":"","institution":"Mapúa University","correspondingAuthor":true,"prefix":"","firstName":"Louie","middleName":"","lastName":"Giray","suffix":""},{"id":617805187,"identity":"ab2c9f04-0572-4377-9563-8fdf89fcc54e","order_by":1,"name":"John Christopher Castillo","email":"","orcid":"","institution":"Mapúa University","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"Christopher","lastName":"Castillo","suffix":""}],"badges":[],"createdAt":"2026-02-20 00:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8921329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8921329/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106521324,"identity":"d9aa280a-7173-49d6-ad4e-5cadb6df4990","added_by":"auto","created_at":"2026-04-09 12:57:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":551555,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8921329/v1/5f5c34d8-da06-4896-9566-95b67a59d1fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Generative AI Integration in Higher Education Teaching: Reflections and Experiences Based on Technological Pedagogical Content Knowledge (TPACK) Model","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eThe rapid evolution of generative artificial intelligence (GenAI) has prompted substantial shifts in higher education worldwide. Emerging evidence demonstrates that tools like ChatGPT are influencing teaching, learning, and assessment practices by automating knowledge generation and transforming interactions between instructors and students [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While GenAI offers significant opportunities to personalize learning and streamline instructional processes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], it simultaneously raises ethical concerns about academic integrity, bias, and overreliance on algorithmic outputs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Amid these developments, faculty readiness and pedagogical competence have become critical factors in determining how effectively GenAI is integrated into higher education contexts [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParallel to these technological advancements, the Technological Pedagogical Content Knowledge (TPACK) framework has gained prominence as a theoretical model describing the complex knowledge required for effective technology integration in teaching [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. TPACK emphasizes the interplay among technological, pedagogical, and content knowledge, underscoring that sustainable innovation depends not only on tool availability but also on teachers' capacity to blend these domains meaningfully [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite growing research on GenAI's potential and challenges, limited studies have systematically explored how college teachers reflect on and conceptualize GenAI adoption through the lens of TPACK. Prior investigations have focused primarily on policy guidelines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], student perspectives [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], or broad institutional strategies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], often overlooking the nuanced ways educators perceive their technological, pedagogical, and content knowledge evolving in response to GenAI. This gap restricts understanding of how teachers situate GenAI within their professional practices and how these reflections inform implementation decisions.\u003c/p\u003e \u003cp\u003eTo address this gap, the present study explores college teachers' reflections and experiences on generative AI integration using the TPACK framework as an analytical lens. This research aims to illuminate how educators negotiate the intersection of AI and pedagogical responsibilities. The findings seek to contribute to strategies that support responsible, context-sensitive adoption of GenAI in higher education.\u003c/p\u003e"},{"header":"2 REVIEW OF RELATED LITERATURE","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Generative AI in Higher Education\u003c/h2\u003e \u003cp\u003eRecent scholarship has documented the transformative yet contested role of generative artificial intelligence (GenAI) in higher education. O'Dea [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] described GenAI as a paradigm shift, offering unprecedented support in knowledge creation while challenging established pedagogical norms. Francis et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] underscored GenAI's duality: it promotes personalized learning and efficient assessment design but risks undermining academic integrity, exacerbating inequalities, and blurring authorship boundaries. Xia et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] noted that GenAI compels rethinking assessment, advocating for authentic tasks, self-regulated learning, and transparency to uphold fairness.\u003c/p\u003e \u003cp\u003eInstitutions have responded variably. McDonald et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] analyzed policies across U.S. universities, finding that while a majority encourage GenAI use, guidance often remains fragmented and burdensome for faculty. Wang et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported that universities increasingly issue sample syllabi and ethics protocols, reflecting a shift from prohibition to conditional integration. Batista et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and Bobula [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] emphasized the need for comprehensive frameworks addressing data privacy, bias, and plagiarism detection. Yusuf et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] further highlighted the cultural variability in perceptions of GenAI's benefits and risks, suggesting that implementation must be context-sensitive to diverse educational traditions and norms.\u003c/p\u003e \u003cp\u003eDespite concerns, GenAI is increasingly integrated into curriculum design, research assistance, and student support services [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lee et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] observed that faculty attitudes are evolving: while some educators express apprehension over workload and ethical dilemmas, many recognize GenAI's inevitability in shaping future skills and institutional competitiveness. The literature converges on the notion that higher education is negotiating a delicate balance\u0026mdash;leveraging GenAI's pedagogical potential while preserving academic standards and equity. Sustained professional development, transparent policies, and collaborative dialogue among stakeholders remain critical for responsible adoption and meaningful innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 College Teachers and Generative AI\u003c/h2\u003e \u003cp\u003eFaculty perspectives on generative artificial intelligence (GenAI) reflect ambivalence, shaped by perceived usefulness, self-efficacy, and institutional readiness. Shata and Hartley [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] reported that perceived usefulness strongly predicts faculty willingness to adopt GenAI, more so than ease of use or technological familiarity. Ellis et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] documented qualitatively diverse experiences: some instructors view GenAI as a catalyst for creative pedagogy, while others fear dependency, student passivity, and erosion of critical thinking.\u003c/p\u003e \u003cp\u003eKhlaif et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] found that performance expectancy, social influence, and facilitating conditions significantly influenced instructors' behavioral intentions to integrate GenAI tools. Ruediger et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] revealed widespread experimentation among faculty, though many lack clear strategies for aligning GenAI with learning objectives. Cacho [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] proposed balanced guidelines to support faculty in navigating ethical dilemmas, assessment integrity, and student engagement.\u003c/p\u003e \u003cp\u003eTeachers have also voiced concerns over increased workload, uncertain institutional expectations, and the challenge of developing new competencies rapidly [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Giray et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] further highlighted that reliance on AI writing detection tools can foster a culture of mistrust and punitive surveillance in higher education [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Their findings emphasize that educators often feel conflicted about using detection technologies, fearing unintended biases against multilingual students and negative impacts on academic relationships.\u003c/p\u003e \u003cp\u003eNonetheless, studies by Baytas and Ruediger [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and Wang et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] emphasize that targeted training, accessible resources, and clear policy frameworks can alleviate apprehensions and foster confidence in using GenAI. Lee et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] further stressed that faculty require sustained support, as many feel underprepared to translate GenAI's capabilities into meaningful learning experiences.\u003c/p\u003e \u003cp\u003eOverall, educators' reflections demonstrate that while GenAI's potential is widely recognized, its sustainable adoption depends on aligning technological tools with pedagogical goals, ethical standards, and discipline-specific content knowledge. Teachers occupy a pivotal role in translating these innovations into responsible, student-centered practices.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 METHODS","content":"\u003cp\u003eWe employed a qualitative research design to investigate the reflections and experiences of faculty members in higher education. This approach allowed for rich, contextual insights into the GenAI tools in academic practice. Data were collected through an online, open-ended survey grounded in the Technological Pedagogical Content Knowledge (TPACK) framework [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The instrument was validated by two experts: one in AI in education and another in educational psychology. In June 2025, twelve faculty members from private universities in Manila, Philippines, participated in the study. The survey explored their experiences and reflections on using GenAI tools in teaching and academic work.\u003c/p\u003e \u003cp\u003e Our participant pool included nine male and three female college teachers, representing a diverse range of academic disciplines (e.g., business, communication, psychology, social work, industrial engineering, literature, and physical education) and career stages within the private higher education sector. Educational backgrounds revealed a highly credentialed group: five participants held master's degrees and seven held doctoral degrees. Their teaching experience ranged from 1 to 21 years, with an average of 12.5 years.\u003c/p\u003e \u003cp\u003eAll participants reported using ChatGPT as their primary GenAI tool. Additional tools mentioned included Gemini, Claude, CoPilot, Grammarly, QuillBot, and Turnitin AI. Faculty members had acquired their knowledge of these tools through self-experimentation, YouTube tutorials, institutional training seminars, and informal peer collaboration. There were 12 participants, which aligns with the typical standard in qualitative research, where data saturation is often reached with 12 to 13 participants [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeanwhile, data were analyzed using Braun and Clarke's [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] six-phase thematic analysis framework, which is particularly suited for exploratory qualitative research. This involved familiarization with the data, initial coding, theme development, theme review, theme definition, and report writing. Also, we adhered to ethical principles governing research involving human participants. Informed consent was obtained from all participants, with clear communication regarding the study's purpose, voluntary participation, and their right to withdraw at any time. Privacy and confidentiality were protected through data anonymization, secure data storage, and full compliance with relevant data privacy regulations.\u003c/p\u003e \u003cp\u003eThis study also has several limitations. The sample size was small and restricted to faculty members from private universities in a specific urban region, which may limit the generalizability of findings. Moreover, while the open-ended survey format allowed for flexible and reflective responses, it limited opportunities for follow-up probing. However, as an exploratory study, the intention was not to generalize but to gain deep insights into the early-stage experiences and reflections of faculty in this rapidly evolving area.\u003c/p\u003e"},{"header":"4 RESULTS","content":"\u003cp\u003eThrough our thematic analysis, we identified five key themes reflecting college teachers' experiences and reflections on GenAI integration in their teaching practices, revealing both the opportunities and challenges they encounter in this evolving educational landscape. The following themes are pragmatic adoption amid ethical tension, pedagogical transformation, vigilance on academic integrity, balancing human connection with GenAI enhancement, and strategic adaptation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Themes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample Statement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePragmatic Adoption Amid Ethical Tensions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty embrace GenAI for efficiency while maintaining ethical boundaries and professional integrity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"When the task is rather repetitive or when I am pressed for time... [However, there are] ethical considerations. I also don't want to be labeled as a professor who is dependent on AI tools.\" - Cris\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePedagogical Transformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenAI catalyzes innovative teaching approaches, enhancing creativity in lesson design and content delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"It made me create more engaging lessons and gave me more time to focus on getting to know the needs of my students better.\" - July\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigilance on Academic Integrity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaintaining rigorous content verification and addressing student over-dependence on AI-generated work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"Review, validate, and revalidate. Conscious effort in keeping the highest level of academic integrity when it comes to generated information and data is imperative.\" - Peter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalancing Human Connection with GenAI Enhancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreserving essential human elements in education while leveraging GenAI's supportive capabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"GenAI is something that we should be prepared [for]. But it cannot replace genuine human connection, most especially in intervening with complex problems.\" - Peter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrategic Adaptation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic evolution of teaching strategies in response to GenAI capabilities and ongoing professional development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"[We must be] knowledgeable with how AI works to effectively gauge the quality of student[s'] output.\" - Martha\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Theme 1: Pragmatic Adoption Amid Ethical Tensions\u003c/h2\u003e \u003cp\u003eCollege teachers demonstrated a pragmatic approach to GenAI adoption. They embraced these tools for efficiency while simultaneously grappling with significant ethical considerations. This theme reflects the ongoing tension between practical benefits and professional integrity concerns that permeates their decision-making processes. Cris articulated this balance clearly, explaining his usage pattern: \"when the task is rather repetitive or when I am pressed for time,\" while simultaneously expressing concern about \"ethical considerations\" and his reluctance to \"be labeled as a professor who is dependent on AI tools.\"\u003c/p\u003e \u003cp\u003eThis pragmatic stance was further evidenced by Mark's approach to tool selection, where he carefully considered \"whether the GenAI tool support[s] our course goals by enhancing critical thinking and personalized learning rather than bypassing student effort.\" Joseph's perspective on industrial applications revealed how disciplinary contexts shape adoption decisions: \"I use GenAI for easier processing as practiced in the industry,\" showing alignment between educational practices and professional standards.\u003c/p\u003e \u003cp\u003eMany college teachers consistently emphasized that GenAI should supplement rather than replace human input and judgment. Peter's reflection captured this sentiment effectively: \"It helped but I never utilized it as a primary source of information,\" while Janice described ChatGPT as \"just a supplement\" for \"research purposes for clarification only [like a book].\" This pragmatic yet cautious approach characterized the overall adoption pattern across all college teachers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Theme 2: Pedagogical Transformation\u003c/h2\u003e \u003cp\u003eGenAI tools have catalyzed significant pedagogical shifts among college teachers, with college teachers reporting enhanced creativity in lesson design and content delivery that altered their teaching approaches. July's experience exemplified this transformation, explaining how GenAI \"made me create more engaging lessons and gave me more time to focus on getting to know the needs of my students better.\"\u003c/p\u003e \u003cp\u003eThe creative enhancement extended beyond lesson planning to assessment strategies, as Martha observed: \"It makes me more creative at assessing my students' level of critical thinking.\" Her reflection reveals how GenAI challenges traditional evaluation methods. Mark's approach to content customization demonstrated this creativity in action: \"AI allows me to customize content based on student profiles,\" indicating a move toward more personalized learning facilitation.\u003c/p\u003e \u003cp\u003eCollege teachers reported using GenAI for diverse creative purposes, from generating analogies to creating varied content formats that cater to different learning styles. Mark explained how he uses \"AI to produce materials in different formats such as text and images to cater to diverse learning needs of students,\" while Gerald observed that \"context and content become more creative\" through AI integration. The transformation also involved rethinking traditional assignments and assessment methods, with Mark noting how GenAI \"did not replace what I teach but it reshaped how I teach,\" leading to more \"student-centered and ethical\" approaches through \"recalibrating teaching materials through AI tools.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Theme 3: Vigilance on Academic Integrity\u003c/h2\u003e \u003cp\u003eA dominant concern across all college teachers was maintaining academic integrity while using GenAI tools. This encompasses both the challenges of preventing student misuse and the imperative for rigorous content validation. James articulated a common frustration experienced by many teachers: \"I get frustrated especially when students are becoming more dependent on AI tools,\" particularly noting the tendency for students to rely on AI rather than developing their own critical thinking skills.\u003c/p\u003e \u003cp\u003eThe imperative for validation emerged as a critical practice among college teachers, who developed systematic approaches to ensure content accuracy and reliability. Peter emphasized the nature of this process: \"review, validate, and revalidate.\" He continued, \"Conscious effort in keeping the highest level of academic integrity when it comes to generated information and data is imperative.\" Mark's approach of treating \"AI content not as a source but only as a draft\" and ensuring verification through \"cross-checking with official and legitimate information sources such as textbooks and peer-reviewed journals\" exemplified this vigilance and established clear protocols for responsible AI use.\u003c/p\u003e \u003cp\u003eCollege teachers demonstrated acute awareness of GenAI's limitations in providing reliable information, which informed their cautious approach to implementation. Peter noted that \"Not all data generated by GenAI is reliable. Some data are misleading,\" while Mike observed that \"[there is still] much content [that] is [incorrect].\" The integrity concerns extended to student behavior, with Mark identifying \"academic integrity\" as his \"main challenge,\" noting the \"tendency for some students to solely rely on AI to completely write their course requirements.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Theme 4: Balancing Human Connection with AI Enhancement\u003c/h2\u003e \u003cp\u003eCollege teachers consistently emphasized the irreplaceable value of human elements in education, while acknowledging GenAI's supportive role, reflecting a nuanced understanding of technology's place in pedagogical practice. This theme captures the ongoing tension between technological advancement and preserving essential human aspects of teaching and learning. Peter's reflection on social work education particularly captured this balance: \"GenAI is something that we should be prepared [for]. But it cannot replace genuine human connection, most especially in intervening with complex problems.\"\u003c/p\u003e \u003cp\u003eThe importance of human touch emerged as particularly crucial in disciplines involving complex human experiences and emotional intelligence. Peter noted that GenAI \"lacked human touch and cannot discuss and comprehend complex human experiences which can only be captured by people who have experienced it firsthand.\" This recognition shaped how college teachers integrated GenAI while deliberately preserving essential human elements in their teaching practice. The limitation was particularly relevant in fields like social work, psychology, and education, where understanding human complexity requires experiential knowledge and emotional intelligence that AI currently cannot replicate.\u003c/p\u003e \u003cp\u003eCollege teachers maintained their focus on developing personal connections with students despite technological integration. July emphasized \"focusing on the needs of my students and how they can learn better in my class,\" while Mike stressed the importance of knowing students personally: \"[You] need to know the children personally.\" This personal dimension remained central to their teaching philosophy despite technological integration, suggesting that effective education requires human understanding that transcends technological capabilities. Martha's concept of \"practice wisdom\" illustrated how human judgment remained essential in evaluating GenAI outputs: \"You know it already when [it's] GenAI who made the analysis for them.\" This experiential knowledge represented an irreplaceable human element that college teachers valued alongside technological capabilities, demonstrating how professional expertise and intuition remain crucial in the AI-integrated classroom.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Theme 5: Strategic Adaptation\u003c/h2\u003e \u003cp\u003eCollege teachers demonstrated remarkable adaptive strategies in response to GenAI capabilities, continuously evolving their approaches based on experience and changing student needs. This theme reflects the dynamic nature of GenAI integration and college teachers' commitment to ongoing professional development in response to technological change. Erasmus's approach exemplified this adaptability: \"Simple identification and take homework will no longer be enough as answers can just be generated [through] AI.\"\u003c/p\u003e \u003cp\u003eThe strategic adaptation involved developing innovative pedagogical approaches that leveraged AI capabilities while maintaining educational rigor. Mark's \"triangular dialogue\" approach, where students \"suggest ideas, AI expands them, while I guide critical evaluation,\" demonstrated how teachers are creating new instructional strategies that emerge from GenAI integration rather than simply adopting existing tools.\u003c/p\u003e \u003cp\u003eMeanwhile, college teachers showed strong commitment to continuous learning about GenAI capabilities and limitations. Mike's experience with training seminars on \"how to properly use ChatGPT\" and learning to avoid AI dependency reflected this learning orientation, while James's learning through \"co-faculty and school webinars\" indicated collaborative approaches to professional development. The strategic dimension extended to preparing students for AI-integrated futures, with Joseph focusing on helping students \"decipher errors and fix them while coding\" and Martha emphasizing the importance of being \"knowledgeable with how AI works to effectively gauge the quality of student[s'] output.\"\u003c/p\u003e \u003c/div\u003e"},{"header":"5 DISCUSSION","content":"\u003cp\u003eThe integration of GenAI tools among college teachers reveals complex negotiations across the three foundational domains of the TPACK framework. We argue that successful GenAI adoption requires understanding of how these knowledge domains intersect and transform in AI-enhanced educational environments.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Content Knowledge (CK) in the GenAI Era\u003c/h2\u003e \u003cp\u003eOur findings reveal that GenAI integration challenges traditional conceptions of content knowledge in higher education. The vigilance on academic integrity demonstrated by participants reflects what we interpret as a profound renegotiation of disciplinary expertise and authority. When Peter emphasizes the need to \"review, validate, and revalidate\" AI-generated content, we observe that college teachers need to understand not just disciplinary content but also how to evaluate AI-generated information within specific academic domains.\u003c/p\u003e \u003cp\u003eThis aligns with Wineburg's [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] work on historical thinking, which emphasizes the importance of source evaluation and critical analysis skills that become even more crucial in AI-mediated information environments. Peter's observation about education that GenAI \"cannot replace genuine human connection\" and \"lacked human touch\" demonstrates domain-specific content knowledge that recognizes the irreplaceable value of experiential and affective understanding. We believe this reflects what Shulman [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] conceptualized as the transformation of content knowledge through pedagogical reasoning. Now, this extended to include AI-mediated content evaluation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strategic adaptation theme reveals how content knowledge evolves to include AI literacy as a fundamental component. Joseph's focus on helping students \"decipher errors and fix them while coding\" exemplifies how disciplinary content knowledge now necessarily includes understanding AI capabilities and limitations. This transformation echoes Prensky's [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] observations about digital literacy evolution, though AI integration presents more complex challenges requiring what Selwyn [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] describes as critical technological literacy that goes beyond operational skills to encompass understanding of technological limitations and biases. Again, we put forward that content knowledge in higher education must expand beyond traditional disciplinary boundaries to encompass critical AI evaluation skills that are domain-specific yet technologically informed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Pedagogical Knowledge (PK) Transformation\u003c/h2\u003e \u003cp\u003eThe pedagogical transformation documented in our themes represents a reconceptualization of teaching knowledge in AI-enhanced environments. July's experience of using GenAI to create \"more engaging lessons\" while gaining \"more time to focus on getting to know the needs of my students better\" demonstrates what we interpret as the evolution of pedagogical knowledge toward more personalized, responsive teaching approaches. This aligns with constructivist pedagogical principles [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] while leveraging AI's capabilities to enhance rather than replace human pedagogical judgment.\u003c/p\u003e \u003cp\u003eMark's development of the \"triangular dialogue\" approach, where \"students suggest ideas, AI expands them, while I guide critical evaluation\" exemplifies sophisticated pedagogical knowledge that integrates AI capabilities into established collaborative learning frameworks. This approach resonates with Laurillard's [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] conversational framework for learning, which emphasizes the importance of dialogue and reflection in educational processes, now extended to include AI as a conversational partner under human guidance.\u003c/p\u003e \u003cp\u003eWe believe that college teachers must understand how to orchestrate learning experiences that leverage AI tools while maintaining pedagogical intentionality and human guidance. The emphasis on human connection revealed in our findings suggests that pedagogical knowledge in the AI era must encompass understanding of when and how to preserve essential human elements in teaching. This finding reinforces Noddings' [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] emphasis on caring relationships in education.\u003c/p\u003e \u003cp\u003eOn the other hand, the creative enhancement in assessment strategies demonstrates pedagogical knowledge evolution toward a more elevated kind of evaluation approaches. Martha's observation that GenAI \"makes me more creative at assessing my students' level of critical thinking\" suggests that pedagogical knowledge must now include understanding how AI tools can enhance rather than compromise educational assessment, but academic rigor must be maintained, still. This aligns with Black and Wiliam's [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] assessment principles, which emphasize the importance of feedback and adaptation in learning processes, now potentially enhanced through AI-supported assessment differentiation and personalization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Technological Knowledge (TK) in Educational Context\u003c/h2\u003e \u003cp\u003eThe technological knowledge demonstrated by participants extends far beyond basic tool proficiency to encompass sophisticated understanding of AI capabilities, limitations, and appropriate educational applications. The pragmatic adoption patterns revealed in our findings suggest that effective technological knowledge in education requires the ability to evaluate technological, especially AI, tools based on educational rather than purely technical criteria.\u003c/p\u003e \u003cp\u003eMark's approach of treating \"AI content not as a source but only as a draft\" demonstrates technological knowledge that encompasses understanding AI's role in educational workflows rather than simply its operational features. This approach to tool integration reflects what Mishra and Koehler [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] describe as the contextual nature of technological knowledge in educational settings, where technical capabilities must be understood in relation to pedagogical goals and content requirements.\u003c/p\u003e \u003cp\u003eThe commitment to continuous learning demonstrated by participants reflects recognition that technological knowledge in rapidly evolving AI contexts requires ongoing professional development. This resonates with Fullan's [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] emphasis on change knowledge and continuous learning as essential components of educational innovation, though AI integration presents unique challenges requiring what Zhao [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] describes as technology fluency that goes beyond basic operational skills to encompass understanding of technological affordances and constraints in educational contexts. Meanwhile, the concern about student dependency on AI tools demonstrates technological knowledge that encompasses understanding of appropriate technology use in educational contexts. This suggests that technological knowledge for college teachers must include not only how to use AI tools effectively but also how to teach students appropriate and ethical AI utilization practices.\u003c/p\u003e \u003cp\u003eBy and large, we find that successful GenAI integration occurs at the intersections of content, pedagogy, and technology, not in isolation. To teach effectively in the age of AI, college teachers must contextually blend these domains. Ethical tensions emerged as participants navigated the balance between efficiency and academic integrity, showing that thoughtful integration requires aligning technological capabilities with pedagogical values and content accuracy. These findings affirm the continued relevance of the TPACK framework, though each domain is evolving in response to AI.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 CONCLUSION","content":"\u003cp\u003eThis study has highlighted how faculty in higher education are navigating the integration of generative AI tools within their teaching practice through the evolving dimensions of the TPACK framework. The results show that successful GenAI use requires more than tool proficiency; it demands a deep rethinking of content, pedagogy, and technology as interdependent domains. Faculty members actively adapted their instructional methods, assessments, and disciplinary thinking, treating GenAI as a collaborator rather than a replacement. At the same time, ethical concerns around academic integrity, student overreliance, and the erosion of human connection were repeatedly emphasized.\u003c/p\u003e \u003cp\u003eThese tensions underscore the need for intentional, critical integration that preserves pedagogical values while embracing innovation. Faculty reflections point toward an emergent model of AI-enhanced teaching that is dynamic, human-centered, and rooted in disciplinary expertise. The TPACK framework remains relevant but must now accommodate the ethical and epistemological shifts that GenAI tools introduce to higher education contexts.\u003c/p\u003e \u003cp\u003e We recommend sustained faculty development on GenAI ethics and literacy, interdisciplinary collaboration for AI-integrated pedagogy, and institutional policies that promote transparent, critical AI use. Future studies should explore longitudinal effects across contexts to support scalable, responsible innovation that upholds educational integrity and centers human judgment in AI-enhanced learning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval\u003c/p\u003e\n\u003cp\u003eThe research was conducted with the knowledge and ethical approval of the corresponding author’s institution: Mapúa University dated May 20, 2025. All procedures involving human participants were performed in accordance with institutional guidelines and recognized ethical standards. Compliance with the Data Privacy Act of 2012 ensured lawful data handling. Participation was voluntary, and respondents were informed of their right to withdraw at any time. No identifying information was collected, and all data were stored in a password-protected database accessible only to the research team. These safeguards protected participants’ privacy, autonomy, and confidentiality.\u003c/p\u003e\n\u003cp\u003eConsent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003eConsent to publish\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eWe declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eEmployment\u003c/p\u003e\n\u003cp\u003eThere are no employment financial interest losses or gains to declare either current or previous for any of the authors.\u003c/p\u003e\n\u003cp\u003eFunding\u003cbr\u003e\u0026nbsp;We received no internal or external funding.\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eData Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eEmployment\u003c/p\u003e\n\u003cp\u003eThere are no employment financial interest losses or gains to declare either current or previous for any of the authors\u003c/p\u003e\n\u003cp\u003eFunding\u003cbr\u003e\u0026nbsp;The authors received no internal or external funding.\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eInformed consent\u003c/p\u003e\n\u003cp\u003eParticipants were informed about the study’s procedures, risks, benefits, and other aspects before their participation. Only those who gave their consent were allowed to participate in the research.\u003c/p\u003e\n\u003cp\u003eAuthors’ contributions\u003c/p\u003e\n\u003cp\u003eLouie led the conceptualization, writing, analysis, manuscript development, critical revisions, and overall direction of the study. John Christopher contributed significantly to the data gathering, literature review, analysis, and manuscript development. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eDisclosure statement\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Including financial and non-financial interests.\u003c/p\u003e\n\u003cp\u003eDeclaration of generative AI use\u003c/p\u003e\n\u003cp\u003eChatGPT 4.0 was used solely for proofreading and enhancing the language of this work. It did not contribute to the development of ideas, arguments, or analysis. The authors take full responsibility for the content and originality of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaytas C, Ruediger D, Making. AI generative for higher education: adoption and challenges among instructors and researchers. New York: Ithaka S\u0026thinsp;+\u0026thinsp;R; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrancis NJ, Jones S, Smith DP. Generative AI in higher education: balancing innovation and integrity. Br J Biomed Sci. 2025;81:14048.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia Q, Weng X, Ouyang F, Lin TJ, Chiu TKF. A scoping review on how generative artificial intelligence transforms assessment in higher education. Int J Educ Technol High Educ. 2024;21:40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiray L. ChatGPT references unveiled: distinguishing the reliable from the fake. Internet Ref Serv Q. 2024;28(1):9\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYusuf A, Pervin N, Rom\u0026aacute;n-Gonz\u0026aacute;lez M. Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. Int J Educ Technol High Educ. 2024;21:21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhlaif Z, Salha S, Salameh N. Higher education instructors' behavioural intention to use ChatGPT: a mixed-methods study. Educ Inf Technol. 2024;29(3):2233\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra P, Koehler MJ. Technological pedagogical content knowledge: a framework for teacher knowledge. Teach Coll Rec. 2006;108(6):1017\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt DA, Baran E, Thompson AD, Mishra P, Koehler MJ, Shin TS. Technological pedagogical content knowledge (TPACK): the development and validation of an assessment instrument for preservice teachers. J Res Technol Educ. 2009;42(2):123\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJim\u0026eacute;nez Sierra \u0026Aacute;A, Ortega Iglesias JM, Cabero-Almenara J, Palacios-Rodr\u0026iacute;guez A. Development of the teacher's technological pedagogical content knowledge (TPACK) from the Lesson Study: a systematic review. Front Educ. 2023;8:1078913.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiray L, Sevnarayan K, Ranjbaran Madiseh F. Beyond policing: AI writing detection tools, trust, academic integrity, and their implications for college writing. Internet Ref Serv Q. 2025;29(1):83\u0026ndash;116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonald N, Johri A, Ali A, Hingle Collier A. Generative artificial intelligence in higher education: evidence from an analysis of institutional policies and guidelines. Comput Hum Behav Artif Hum. 2025;3:100121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiray L, Aquino R. Use and impact of ChatGPT on undergraduate engineering students: a case from the Philippines. Internet Ref Serv Q. 2024;28(4):453\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis R, Han F, Cook H. Qualitatively different teacher experiences of teaching with generative artificial intelligence. Int J Educ Technol High Educ. 2025;22:33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Dea X. Generative AI: is it a paradigm shift for higher education? Stud High Educ. 2024;49(5):811\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Dang A, Wu Z, Mac S. Generative AI in higher education: seeing ChatGPT through universities' policies, resources, and guidelines. Comput Educ Artif Intell. 2024;7:100326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBatista J, Mesquita A, Carnaz G. Generative AI and higher education: trends, challenges, and future directions from a systematic literature review. Information. 2024;15(11):676.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBobula M. Generative artificial intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications. J Learn Dev High Educ. 2024;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShata A, Hartley K. Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI. Int J Educ Technol High Educ. 2025;22:14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis R, Han F, Cook H. Sustainability considerations for AI-supported teaching practices in higher education. Sustainability. 2025;17(4):14590.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee D, Arnold M, Srivastava A, Plastow K, Strelan P, Ploeckl F, Lekkas D, Palmer E. The impact of generative AI on higher education learning and teaching: a study of educators' perspectives. Comput Educ Artif Intell. 2024;6:100221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuediger D, Blankstein M, Love S, Generative. AI and postsecondary instructional practices: findings from a national survey of instructors. New York: Ithaka S\u0026thinsp;+\u0026thinsp;R; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCacho RM. Integrating generative AI in university teaching and learning: a model for balanced guidelines. Online Learn. 2024;28(3):55\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiray L, De Silos PY, Adornado A, Buelo RJV, Galas E, Reyes-Chua E, Santiago C, Ulanday ML. Use and impact of artificial intelligence in Philippine higher education: reflections from instructors and administrators. Internet Ref Serv Q. 2025;28(3):315\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiray L. The problem with false positives: AI detection unfairly accuses scholars of AI plagiarism. Ser Libr. 2024:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaja P. What's a good sample size for qualitative research? Wynter [Internet]. 2025 Jan 4 [cited 2026 Feb 20]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wynter.com/post/a-good-sample-size-for-qualitative-research\u003c/span\u003e\u003cspan address=\"https://wynter.com/post/a-good-sample-size-for-qualitative-research\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWineburg S. Historical thinking and other unnatural acts: charting the future of teaching the past. Philadelphia (PA): Temple University; 2001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShulman LS. Knowledge and teaching: foundations of the new reform. Harv Educ Rev. 1987;57(1):1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrensky M. Digital natives, digital immigrants. Horizon. 2001;9(5):1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelwyn N. Is technology good for education? Cambridge (UK): Polity; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVygotsky LS. Mind in society: the development of higher psychological processes. Cambridge (MA): Harvard University Press; 1978.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaurillard D. Teaching as a design science: building pedagogical patterns for learning and technology. New York (NY): Routledge; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoddings N. The challenge to care in schools: an alternative approach to education. 2nd ed. New York (NY): Teachers College; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlack P, Wiliam D. Assessment and classroom learning. Assess Educ Princ Policy Pract. 1998;5(1):7\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFullan M. The new meaning of educational change. 4th ed. New York (NY): Teachers College; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y. What teachers need to know about technology? Framing the question. In: Zhao Y, editor. What should teachers know about technology: perspectives and practices. Greenwich (CT): Information Age Publishing; 2003. pp. 1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"academic integrity, AI literacy, pedagogy, TPACK","lastPublishedDoi":"10.21203/rs.3.rs-8921329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8921329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated how college faculty integrate generative AI (GenAI) tools into their teaching practices, focusing on their reflections through the Technological Pedagogical Content Knowledge (TPACK) lens. Results revealed that GenAI integration transforms all three TPACK domains. In content knowledge, teachers emphasized the need to critically assess AI-generated material, signaling a shift toward AI-informed disciplinary expertise. In pedagogical knowledge, participants used GenAI to personalize instruction, enhance assessments, and create new forms of student engagement, while maintaining a strong emphasis on human connection and academic rigor. In technological knowledge, faculty demonstrated adaptive use of GenAI, distinguishing between AI's generative capabilities and the pedagogical judgment needed to guide them ethically and effectively. Ethical concerns surfaced around dependency, authenticity, and integrity. This underscores the need for thoughtful integration that aligns technological possibilities with pedagogical intent and content accuracy. Overall, successful GenAI use occurred when teachers actively blended all three knowledge areas, not when relying on technology alone. These results affirm the evolving relevance of the TPACK model and highlight the need for ongoing, values-driven faculty development.\u003c/p\u003e","manuscriptTitle":"Generative AI Integration in Higher Education Teaching: Reflections and Experiences Based on Technological Pedagogical Content Knowledge (TPACK) Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 12:55:01","doi":"10.21203/rs.3.rs-8921329/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"79620081016709495120959252134365958304","date":"2026-04-05T13:59:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311708279429523080434552977570777693036","date":"2026-04-04T19:17:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-03T13:18:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-20T04:23:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-20T04:21:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Artificial Intelligence","date":"2026-02-20T00:04:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74225fa4-b8ca-4ee9-afd4-10d6f0e6e69b","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T12:55:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 12:55:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8921329","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8921329","identity":"rs-8921329","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}