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22 SES 06 C: AI and Pedagogy
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22. Research in Higher Education
Paper Exploring Value Judgements in Grading: Will Teachers Mark Down Student Work Assisted by GenAI, and Should They? 1: Education University of Hong Kong, Hong Kong S.A.R. (China); 2: Deakin University, Australia Presenting Author:Grading is a critical aspect of higher education, connected closely with student learning, credentialing, and institutional accountability. More recently, the widespread use of generative AI (GenAI) among university students has introduced new layers of complexity to teachers’ grading practices. For example, do students who complete their work without GenAI assistance deserve a higher grade? How can teachers disentangle and properly weigh the respective contributions of GenAI versus the unique insights students bring to their work? If a student declares GenAI assistance in their work, does this make their work less original or less independent? These questions are now arguably central to every teacher’s grading decision but come with no straightforward answer. One key reason for this is that the rise of GenAI has complicated and called into question many fundamental concepts underlying teachers’ grading decisions, including what signifies the quality of student work and their academic achievement. As teachers make their grading decisions, they are also at the same time making value judgements heavily mediated by their own assumptions about what is important, ethical, or desirable in the grading (Sun & Cheng, 2013). By teacher value judgement, it refers to any decision that reflects a teacher’s subjective prioritisation of values in the grading process. These values may be implicit and often not openly discussed, such as student effort and behaviour. Values may also be externally prescribed, such as those formalised in rubrics – however, making the value judgement would still involve teachers bringing their own interpretations to the rubric, which are influenced by their individual priorities and the practical constraints they experience. At present, however, little is known about how university teachers navigate and make these value judgements, how they balance various, sometimes even competing values, and what they prioritise in the grading of GenAI-assisted work. While much of the scholarly discussion in higher education has centred on academic misconduct, the reality faced by teachers is far more complex than making a binary decision on whether particular uses of GenAI are acceptable in student work. Therefore, this study addresses an important gap in higher education literature by investigating university teachers’ grading practices in a time when GenAI can mediate students’ work quality to varying degrees. We propose to address the following research question: What value judgements do teachers make when determining the grades of students who (may) have used GenAI to assist in their work? Methodology, Methods, Research Instruments or Sources Used This study engaged 33 university teachers in scenario-based interviews to investigate their grading practices in a time when GenAI can mediate students’ work quality to varying degrees. The interview consisted of two parts and typically lasted between 45 to 60 minutes. The first part asked general questions such as the participants’ beliefs about GenAI and grading, the types of assignments and any GenAI-related policies in their courses. However, these questions, although necessary to set the scene, often result in general statements from teachers that contribute little to elucidate their value judgements. Therefore, the second part invited teachers to respond to several controversial grading scenarios related to the use of GenAI in student work. These grading scenarios were derived from recent research on the challenges around students’ GenAI use in assignments and informed by the format (e.g., multiple-choice questions) suggested by the two earlier research which used controversial grading scenarios to elicit teachers’ value judgements around grading (i.e., Brookhart, 1993; Sun & Cheng, 2013). The purpose is not to elicit “correct” answers from teachers or to quantify their answers, but to provide a grounding for teachers to articulate their judgement process in a more concrete, contextualised manner. We acknowledge that teachers’ decision-making captured in this study did not carry the same stakes as real-world grading. However, interviewing teachers about their real-world grading decisions may not effectively reveal the implicit assumptions teachers hold about student work and grading, as many are reluctant to open their grading to scrutiny due to fear of critique. By using these hypothetical scenarios, this research design creates a low-pressure environment that allows teachers to share their genuine thoughts. Conclusions, Expected Outcomes or Findings Data show that teachers do make value judgements of student work, which extend beyond the assignment itself to encompass teachers’ conjecture about who the student is (person-oriented values), what they are capable of (capability-oriented values), how they relate to others (relation-oriented values), and whether the grading decision leads to good outcomes (justice-oriented values). Many non-academic values are prioritised in the grading (e.g., honesty, diligence, trust), and there are significant variations across teachers’ value judgements. The study points to a messy grading space full of tension and inconsistency. If left unaddressed, this will likely result in many unintended consequences, such as distrust from students, biased grading and weakened credibility of academic certifications. We foreground validity as an important concept (Dawson et al., 2024) to help teachers navigate this complex grading landscape and call for greater transparency about how students’ GenAI use will be factored into teachers’ grading decisions. The study seeks to move beyond the binary debate of whether GenAI use in student work constitutes cheating, towards a nuanced investigation into the subjectivities of grading in the age of GenAI. References Brookhart, S. M. (1993). Teachers' grading practices: Meaning and values. Journal of Educational Measurement, 30(2), 123-142. Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024). Validity matters more than cheating. Assessment & Evaluation in Higher Education, 1-12. Sun, Y., & Cheng, L. (2013). Teachers’ grading practices: meaning and values assigned. Assessment in Education: Principles, Policy & Practice, 21(3), 326-343. 22. Research in Higher Education
Paper Between Shortcut and Insight: Generative AI as a Dialogic Partner in Higher Education Teaching Åbo Akademi, Finland Presenting Author:Generative AI has rapidly transformed the conditions for teaching, studying, writing and learning in higher education. GenAI makes it possible to produce academically sounding texts without engaging in the reflective processes through which understanding traditionally has been formed (Ostenson & Allred, 2025). This development raises fundamental questions about what counts as knowledge, how learning and teaching are understood, and what role higher education should play. On the one hand, research suggests that reliance on GenAI may reduce students’ capacity for creative and critical thinking, as cognitive tasks are increasingly offloaded to machines (Pikhart & Al-Obaydi, 2025; Toma & Yánez-Pérez, 2024). On the other hand, studies also indicate that students’ creative and critical thinking can be deepened when they actively engage GenAI in their studies (Mollick & Mollick, 2023; Wang & Fan, 2025). Given the lack of conclusive findings, Wegerif and Casebourne (2025) argue that technologies such as GenAI may, on the one hand, open possibilities for deeper understanding, while on the other hand also generating challenges such as dependency, automation of thought, and loss of agency. They suggest that the implications of GenAI use are closely tied to how it is pedagogically framed and integrated in higher education teaching. In other words, this shifts the focus from technology in itself to teaching practices and pedagogical design. In this study, GenAI is approached from a dialogic perspective (Wegerif & Casebourne, 2025). Dialogue is here not dependent on all participants being conscious, intentional or morally responsible. Rather, dialogue is understood as a pedagogical and phenomenological space in which meaning emerges through encounters with otherness. GenAI can participate in such processes by introducing alternative formulations, unexpected perspectives or conflicting interpretations that invite critical and reflective thinking. From this perspective, learning is understood as a process in which meaning is created in and through dialogue, rather than as the transmission of knowledge. GenAI is therefore not regarded as a tool for delivering answers, but as an actor within pedagogical interactions that can both open and constrain opportunities for reflection and critical thinking. The aim of this study is to explore how higher education teaching can be developed by focusing on students’ use, understanding, and meaning-making when engaging with generative AI (GenAI) as a dialogic partner. The research question for the study is: How do students use and understand generative AI as a dialogic partner, and how can these practices inform the development of higher education teaching? The study is grounded in practice-based action research (Carr & Kemmis, 1986) and builds on my teaching in university courses on educational leadership. These courses provide concrete settings where teaching practices involving GenAI are tried out, reflected upon, and gradually developed over time. Within these courses, GenAI is integrated as a dialogic partner in both teaching and students’ academic work, and teaching practices are iteratively developed as part of the action research process. This study originates from the first author’s experiences of teaching in higher education at a time when generative AI has become an increasingly present part of students’ academic work and in society more broadly. While generative AI involves significant ethical concerns related to sustainability, environmental impact, and authorship, the study is based on the assumption that higher education cannot meaningfully opt out of engaging with such technologies, as they are already widely used in contemporary society. Rather than avoiding generative AI, the study approaches these concerns as issues to be addressed critically and pedagogically in teaching. Methodology, Methods, Research Instruments or Sources Used Against this background, the study is grounded in practice-based action research (Carr & Kemmis, 1986). The overall aim is to develop the first author’s teaching by integrating GenAI as a dialogic partner in both instruction and students’ academic work. The first author approaches the project primarily as a teacher examining and developing his own teaching practice on a research-informed basis. The second author contributes expertise in AI, theoretical framing, and qualitative analysis and serves as a critical friend throughout the research process. The action research project is conducted across at least three iterative cycles: the autumn of 2025, the spring of 2026, and the autumn of 2026. Each cycle involves the planning, enactment, observation, and reflection of teaching activities that include GenAI as part of regular coursework. Each cycle builds on the previous one, with tasks and AI use adjusted based on what emerges in practice. The research is situated within a higher education programme in Pedagogical Leadership and Development (PLU). Across the courses, the learning objectives focus on developing an understanding of leadership in educational contexts. In the courses, GenAI is integrated into ordinary teaching activities and used in seminar discussions, written assignments, and a concluding reflection, in which students are asked to consider how GenAI has supported or challenged their work in relation to the course learning objectives. All students were informed about the project before the courses began. Participation was voluntary, informed consent was obtained, and students could withdraw at any time without consequences for teaching, assessment, or grading. All student material was anonymised, and only data from consenting participants were included. The study followed the ethical guidelines of TENK and Åbo Akademi University regarding research ethics, anonymity, and voluntary participation. Different student groups participated across the action research cycles. In the first cycle, approximately 30 students at bachelor’s and master’s levels were involved. The second cycle included approximately 15 bachelor’s students. The data was collected during three courses in pedagogical leadership. Conclusions, Expected Outcomes or Findings The first cycle was exploratory and focused on examining how GenAI could be used to develop students’ understanding of course content through dialogic interaction. Based on the first cycle, there are some preliminary insights, although analysis is still ongoing. One preliminary finding concerns the conditions for pedagogically productive dialogues with GenAI. While students generally engaged with the AI in an interested manner, many dialogues remained relatively short or superficial, according to the student themselves. In practice, many asked the GenAI a question or two, got a well formulated answer and continued the discussion between students. After discussing this in the course, it appears to me as important to have clear structures on the assignment, provide examples of follow-up questions, and guidance on how to continue the dialogue, both in terms of width and breadth. Students also highlighted the need for caution, as the aesthetic and fluent form of AI-generated text may create an illusion of depth, making such texts appear more convincing and factual than they are. Overall, students found it valuable to work with GenAI as part of the course. In the next action research cycle, the teaching will therefore place greater emphasis on providing background knowledge about GenAI as a dialogic partner, what such dialogue can involve, and how to engage in academically oriented discussions with AI. References Carr, W., & Kemmis, S. (1986). Becoming critical: Education, knowledge and action research. London: Falmer. Finnish National Board on Research Integrity. (2019). The ethical principles of research with human participants and ethical review in the human sciences in Finland. Tenk Publications. Mollick, E. & Mollick, L. (2023) Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts. The Wharton School Research Paper. http://dx.doi.org/10.2139/ssrn.4391243 Ostenson, J. & Allred, J. (2025). Thinking and Writing with AI as a Dialogue Partner. The Utah English Journal: 53(19). https://scholarsarchive.byu.edu/uej/vol53/iss1/19 Pikhart, M. & Al-Obaydi, L. (2025). Reporting the potential risk of using AI in higher Education: Subjective perspectives of educators. Computers in Human Behavior Reports. https://doi.org/10.1016/j.chbr.2025.100693 Toma, R.B., Yánez-Pérez, I. (2024). Effects of ChatGPT use on undergraduate students’ creativity: a threat to creative thinking?. Discov Artif Intell 4(74). https://doi.org/10.1007/s44163-024-00172-x Wang, J. & Fan, W. (2025) The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanit Soc Sci Commun 12(621). https://doi.org/10.1057/s41599-025-04787-y Wegerif, R. & Casebourne, I. (2025). A dialogic theoretical foundation for integrating generative AI into pedagogical design. British Journal of Educational Technology, 00, 1–16. https://doi.org/10.1111/bjet.70026 22. Research in Higher Education
Paper A Framework for Teaching and Learning Responsible Use of Large Language Model-based AI Tampere University, Finland Presenting Author:Large language model (LLM)-based artificial intelligence tools, such as ChatGPT, Claude, and Microsoft Co-Pilot, have taken the world by storm, leaving universities playing catch-up to regulate and guide students in the use of these algorithmic aids. Universities recognise the opportunities these tools offer for teaching and learning, as well as the importance of mastering them in future job markets (Digital Education Council, 2025). Yet, careless adoption of these tools carries significant cost and risk at several fronts: first, to civic institutions (Hartzog & Silbey, 2025); second, to natural resources (de Vries-Gao, A. 2025; International Energy Agency, 2025; Jegham et al., 2025); third, to epistemic resources (Dammu et al., 2024; Sommerer, 2024), and fourth, to students’ cognitive (Gerlich, 2025; Lee et al., 2025) and behavioural development (Zhou & Zhang, 2024; Kooli et al., 2025). As such, choosing appropriate use cases for LLM-based AI tools (when, where and how) is an everyday challenge for university teachers and students alike. In the rest of the text, I refer to these tools with the term ‘AI tools’ in the interest of brevity and in concordance with the common practice, despite large language models representing only a small, even if topical, aspect of the vast field of artificial intelligence. Building on the concept of digital competence (Ilomäki et al., 2016; Kasperski et al., 2022; Spante et al., 2018; Zhao et al., 2021), my research explores the foundations of responsible large language model use. Specifically, I develop a conceptual framework that foregrounds two key aspects of responsible use. First, the framework foregrounds the development of students’ ability to use AI in a meaningful way, encouraging consideration of the purpose, process and audience of a task alongside its output. Second, the framework supports the development of students’ ability to critically evaluate AI tools and the cost of their use, increasing the awareness of the manifold implications for natural and epistemic sustainability. Methodology, Methods, Research Instruments or Sources Used My paper explores a conceptual framework of responsible AI use. On this conceptual analysis, I draw on pedagogical literature on digital and AI competence (e.g., Ilomäki et al., 2016; Kasperski et al., 2022; Spante et al., 2018; Zhao et al., 2021) as well as on a broad range of interdisciplinary research on large language model development, use, and impact, including computer science, environmental studies, and psychology. As a part of this work, I am preparing an action research project as the next empirical research step (Niemi, 2011; Norton, 2009). I aim to have the initial results of this phase of the study by August 2026 and the Emerging Research Conference. Conclusions, Expected Outcomes or Findings The ubiquity of AI tools is risking the sustainability transition in society (Bush et al., 2025). My responsible AI use framework aims to contribute to a sustainable AI practice in the short term by providing educators with a tool for teaching interventions and, in the long term, with rigorous empirical research that informs education and has the potential to influence policy and educational practice. References Bush, A., Aksoy, M., Pauly, M., Ontrup, G. (2025) Choosing a model, shaping a future: Comparing LLM perspectives on sustainability and its relationship with AI. arXiv:2505.1443. Dammu, P.P.S., Jung, H., Singh, A., Choudhury, M., Mitra, T. (2024) “They are uncultured”: Unveiling covert harms and social threats in LLM generated conversations. arXiv:2405.05378v1. de Vries-Gao, A. (2025) Artificial intelligence: Supply chain constraints and energy implications, Joule. Digital Education Council. (2025). AI in the Workplace 2025. Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6 Hartzog, W., & Silbey, J. M. (2025). How AI Destroys Institutions. Boston Univ. School of Law Research Paper No. 5870623. Ilomäki, L., Paavola, S., Lakkala, M., & Kantosalo, A. (2016). Digital competence–an emergent boundary concept for policy and educational research. Education and information technologies, 21(3), 655-679. International Energy Agency. 2025. Energy and AI. IEA Publications. Jegham, N., Elmoubarki, L., Abdelatti, M., Koh, C. Y., Hendawi, A. (2025). How hungry is AI? Benchmarking energy, water, and carbon footprint of LLM inference. arXiv:2505.09598v6. Kasperski, R., Blau, I., & Ben-Yehudah, G. (2022). Teaching digital literacy: Are teachers’ perspectives consistent with actual pedagogy? Technology, Pedagogy and Education, 31(5), 615-635. Kooli, C., Kooli, Y., & Kooli, E. (2025). Generative artificial intelligence addiction syndrome: A new behavioral disorder? Asian Journal of Psychiatry, 107, 104476. Lee, H. P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025, April). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. In Proceedings of the 2025 CHI conference on human factors in computing systems (pp. 1-22). Niemi, R. (2019). Five approaches to pedagogical action research. Educational Action Research, 27(5), 651-666. Norton, L. (2009). Action research in teaching and learning: A practical guide to conducting pedagogical research in universities. Routledge. Sommerer, T. (2025). Baudrillard and the Dead Internet Theory. Revisiting Baudrillard’s (dis) trust in Artificial Intelligence. Philosophy & Technology, 38(2), 54. Spante, M., Hashemi, S. S., Lundin, M., & Algers, A. (2018). Digital competence and digital literacy in higher education research: Systematic review of concept use. Cogent Education, 5(1), 1519143. Zhao, Y., Llorente, A. M. P., & Gómez, M. C. S. (2021). Digital competence in higher education research: A systematic literature review. Computers & education, 168, 104212. Zhou, T., & Zhang, C. (2024). Examining generative AI user addiction from a CAC perspective. Technology in Society, 78, 102653. 22. Research in Higher Education
Paper Correlation of Knowledge, Uses and Stances of University teachers on GenAI Universitat Autònoma de Barcelona - CRiEDO, Spain Presenting Author:The irruption of Generative AI (GenAI) is rapidly reshaping higher education. The widespread availability of tools capable of generating text, images, code or feedback has introduced profound challenges for teaching and assessment, including concerns about academic integrity, authorship, bias or data privacy. At the same time, GenAI may bring significant pedagogical potentialities, such as enhanced support for learning processes, instructional design, content creation and student engagement. In this context, higher education teachers play a pivotal role, yet their position remains insufficiently understood. The rapid diffusion of GenAI raises critical questions regarding teachers’ knowledge, use and stance: What do teachers know about GenAI? Are they using it for teaching, and how? How do they position themselves on the integration of GenAI in higher education? To address these issues, researchers have begun to investigate GenAI in higher education, focusing on institutional responses, ethical debates, student perspectives, and so on. With regards to their knowledge, studies consistently report high general awareness of GenAI tools among university teachers, with many having experimented with them at least occasionally (Ghimire et al., 2024). However, this familiarity often masks important gaps in AI literacy. While teachers tend to understand GenAI’s basic capabilities and recognize its potential for efficiency and pedagogical support, deeper technical understanding remains limited outside the specialized fields. Additionally, research highlights persistent uncertainty regarding pedagogical integration: many teachers report difficulties redesigning assessment, feedback, and learning outcomes in ways that meaningfully and responsibly incorporate GenAI (Cordero et al., 2024; Nikolic et al., 2024). As for the teachers’ use, recent research shows that higher education teachers are already engaging with GenAI, primarily to augment or ease existing teaching practices rather than to transform pedagogical models. The most common uses involve content creation and course design, such as generating teaching materials, examples, quizzes or media resources, as well as providing automated or semi-automated feedback in some cases. GenAI is also increasingly framed as a learning support tool, for instance as a “virtual tutor” to assist students with brainstorming, drafting, explanation, or research tasks, often within teacher-guided activities aimed at fostering critical and ethical use. While more advanced, collaborative student-AI-teacher models are emerging, these remain limited and uneven across disciplines, with stronger uptake reported in engineering, health, language, and creative fields (Quian, 2025). Regarding stance, the literature portrays higher education teachers as cautiously positive rather than resistant. Across contexts, most view GenAI as a supportive tool that can enhance teaching and learning, not as a replacement for educators (Nikolic, 2024). At the same time, they express strong concerns about academic integrity, data privacy, bias, over-reliance by students, and the reliability of AI outputs. Faculty acceptance of GenAI appears closely tied to perceived pedagogical value, ease of use, ethical clarity, and the availability of institutional support (Tovar & Ocegueda, 2025). Overall, existing studies suggest a landscape characterized by experimentation, uneven skills, and ambivalent optimism, underscoring the need for further empirical research into teachers’ knowledge, use and stance in relation to GenAI in higher education. Even though some incipient findings have been reported about higher education teachers’ knowledge, use and stance, there are still some questions about if these dimensions are interconnected. In this scenario, the objective of this paper is to analyse whether the teacher’s knowledge, uses and stances on GenAI are somehow related. The research questions are:
Methodology, Methods, Research Instruments or Sources Used The methodology of this paper is quantitative. The instrument used to collect the information needed to answer the research questions is EdU-P-InA survey (Mercader et al., 2025). This survey was elaborated adhoc in order to collect information about the knowledge, uses and stances of university teachers regarding GenAI in education. The initial survey underwent a pilot test (N= 14) and its improved version was validated by 18 judges regarding its clarity, appropriateness, importance and sufficiency. The final version of the survey consists of 36 questions distributed in 3 dimensions: Knowledge (11 questions), Uses (14 questions) and Stances (11 questions). Regarding its reliability, Cronbach’s Alfa of EdU-P-InA shows a strong consistency with an Alfa of .906. The data was collected between March and June 2025. Data analysis was carried out with the support of SPSS software (v31) and consisted of descriptive analysis (means and standard deviation) as well as inferential analysis (Pearsons’ correlation) to explore the possible relationships within the same dimension and between dimensions. The population of the study were university teachers from 6 public universities in Spain with different territory reach (North, South, East, West, Center and Online). The sample obtained was 730 teachers, distributed according to the size of their universities. With regards to the field of knowledge, representation across academic disciplines is balanced, considering that some disciplines have more teaching staff than others. In this regard, 38.6% are from Social Sciences and Law, 24.1% from Science and Engineering, 20.1% from Arts and Humanities, and 17.1% from Health Sciences. In terms of gender, the sample is mainly female (47.4%) and male (50%), although non-binary individuals (1.1%), individuals who prefer not to answer (1.4%), and others (0.1%) are also included. The mode in age and teaching experience are 50 and 10 years, respectively, although the average is 48.28 years (SD = 10.36) and 17.09 years of experience (SD = 11.08). The teaching staff who participated are mainly full-time and permanent employees (60.4%), representing the different professional categories (pre-doctoral, post-doctoral, assistant, tenured, professor, visiting, associate, substitute and others). Conclusions, Expected Outcomes or Findings The internal correlations of each dimension (Knowledge, Uses and Stances) show significant association (p < .050), which are moderate in Uses and Stances, and strong in the Knowledge dimension. All these associations are positive, except for concerns about bias, plagiarism and GenAI limitations related with the perception of usefulness and efficiency of GenAI for teaching and learning. Although the relations are significant, the correlations are very weak (r < .200). The correlations between Knowledge and Stances items are significant but all of them are weak or very weak, as well as Stances and Use. The exception is considering GenAI useful for teaching, which moderately correlates with using GenAI generative text [r(728 = .474), p = .000] and using it frequently to teach, [r(728 = .403), p = .000]. Knowledge and Use are two dimensions that have greater force in their relationship, although only five of them are moderate: knowing how to help students to use GenAI confidently, [r(728 = .411), p = .000] and having greater GenAI competency correlates with using it for planning [r(728 = .435), p = .000], teaching [r(728 = .456), p = .000], with students [r(728 = .474), p = .000] and generating text [r(728 = .472), p = .000]. Therefore, teachers considering that GenAI is helpful, or being worried about it is not based on their knowledge. The fact that teachers are used to implement technology without needed to know how it works might also be a reason why there are no correlations. However, GenAI is not the same as previous technological resources so teachers having basic GenAI literacy is a must to be able to implement it in their teaching with confidence, security, consistency and ethically. References Ghimire, A., Prather, J., & Edwards, J. (2024). Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors. 2024 IEEE Frontiers in Education Conference (FIE), 1-9. https://doi.org/10.1109/fie61694.2024.10892891 Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., Jones, R., Ros, M., & Middleton, R. (2024). A systematic literature review of attitudes, intentions and behaviours of teaching academics pertaining to AI and generative AI (GenAI) in higher education: An analysis of GenAI adoption using the UTAUT framework. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.9643 Cordero, J., Torres-Zambrano, J., & Cordero-Castillo, A. (2024). Integration of Generative Artificial Intelligence in Higher Education: Best Practices. Education Sciences. https://doi.org/10.3390/educsci15010032 Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x Qian, Y. (2025). Pedagogical Applications of Generative AI in Higher Education: A Systematic Review of the Field. TechTrends, 69, 1105 - 1120. https://doi.org/10.1007/s11528-025-01100-1. Tovar, I., & Ocegueda, G. (2025). Attitudes of University Professors towards the Use of Artificial Intelligence in Teaching and Learning. INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS. https://doi.org/10.47191/ijmra/v8-i01-46 | ||