Student perception of the competencies of teachers who integrate AI
DOI:
https://doi.org/10.36390/telos281.09Keywords:
Teaching competencies, artificial intelligence, student perception, distributed agency, higher educationAbstract
This article places the discussion of teachers who integrate AI within a framework where professional legitimacy depends less on instrumental skills and more on the visible governance of human-AI co-agency. To identify the competencies that sustain this legitimacy from the student perspective, a qualitative study was conducted with narratives from 3,120 students from the University of Guadalajara (Mexico) and the Technical University of Machala (Ecuador). An open-ended question was asked, and content analysis was performed following the Krippendorff criteria. The results show a linkage of four competencies: (1) pedagogical relationship and empowerment (personalization and feedback with explicit criteria and limits); (2) technical-operational mastery (design and auditing of flows with AI, curation, and documentation); (3) knowledge curation and didactic design with AI: clarity and innovation (translation of recent and diverse evidence into understandable sequences and active experiences); and (4) humanization of knowledge and ethical reflection (transparency, non-delegable points, and explainability of decisions). It is concluded that these competencies express the transition from "digital competence" to a governed distributed agency: AI expands capabilities, while pedagogical judgment organizes and guides.
Downloads
References
Ali, I., Warraich, N. F., & Butt, K. (2025). Acceptance and use of artificial intelligence and AI-based applications in education: A meta-analysis and future direction. Information Development, 41(3), 859–874. https://doi.org/10.1177/02666669241257206
Almashour, M., Aldamen, H. A. K., & Jarrah, M. (2025). “They know AI, but they also know us”: student perceptions of EFL teacher identity in AI-enhanced classrooms in Jordan. Frontiers in Education, 10(1611147). https://doi.org/10.3389/feduc.2025.1611147
Ampofo, J., Bentum-Micah, G., Xusheng, Q., Sun, B., & Mensah Asumang, R. (2025). Exploring the role of teacher empathy in student mental health outcomes: a comparative SEM approach to understanding the complexities of emotional support in educational settings. Frontiers in Psychology, 16, 1503258.https://doi.org/10.3389/fpsyg.2025.1503258
Baidoo-Anu, D., Asamoah, D., Amoako, I., & Mahama, I. (2024). Exploring student perspectives on generative artificial intelligence in higher education learning. Discover Education, 3(1). https://doi.org/10.1007/s44217-024-00173-z
Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in the higher education: A systematic literature review and research challenges. International Journal of Educational Research, 127(102411), 102411. https://doi.org/10.1016/j.ijer.2024.102411
Barad, K. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs, 28(3), 801–831. https://doi.org/10.14361/9783839403365-008
Barad, K. (2012). On touching—the inhuman that therefore I am. Differences, 23(3), 206–223.
Beale, R. (2025). The revolution has arrived: What the current state of large language Models in education implies for the future. In arXiv [cs.HC]. arXiv. http://arxiv.org/abs/2507.02180
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-023-00436-z
Bozalek, V. (2022). Uncertainty or indeterminacy? Reconfiguring curriculum through agential realism. Education as Change. https://doi.org/10.25159/1947-9417/11507
Brianza, E., Schmid, M., Tondeur, J., & Petko, D. (2022). Situating TPACK: A systematic literature review of context as a domain of knowledge. Contemporary Issues in Technology and Teacher Education, 22(4), 707–753.
Caena, F., & Redecker, C. (2019). Aligning teacher competence frameworks to 21st century challenges: The case for the European Digital Competence Framework for Educators ( Digcompedu) . European Journal of Education, 54(3), 356–369. https://doi.org/10.1111/ejed.12345
Chan, C. K. Y., & Tsi, L. H. Y. (2024). Will generative AI replace teachers in higher education? A study of teacher and student perceptions. Studies in Educational Evaluation, 83(101395), 101395.
Chee, H., Ahn, S., & Lee, J. (2024). A competency framework for AI literacy: Variations by different learner groups and an implied learning pathway. British Journal of Educational Technology: Journal of the Council for Educational Technology. https://doi.org/10.1111/bjet.13556
Chiu, T. K. F., & Rospigliosi, P. ’asher'. (2025). Encouraging human-AI collaboration in interactive learning environments. Interactive Learning Environments, 33(2), 921–924. https://doi.org/10.1080/10494820.2025.2471199
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210(104967), 104967.https://doi.org/10.1016/j.compedu.2023.104967
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227(105224), 105224.https://doi.org/10.1016/j.compedu.2024.105224
Gee, J. P. (2014). An introduction to discourse analysis: Theory and method (4th ed.). Routledge. https://doi.org/10.4324/9781315819679
Grájeda, A., Córdova, P., Córdova, J. P., Laguna-Tapia, A., Burgos, J., Rodríguez, L., Arandia, M., & Sanjinés, A. (2024). Embracing artificial intelligence in the arts classroom: understanding student perceptions and emotional reactions to AI tools. Cogent Education, 11(1). https://doi.org/10.1080/2331186x.2024.2378271
Hammoda, B., & Foli, S. (2024). A digital competence framework for learners (DCFL): A conceptual framework for digital literacy. Knowledge Management & E-Learning: An International Journal. https://eric.ed.gov/?id=EJ1446343
Han, B., Coghlan, S., Buchanan, G., & McKay, D. (2025). Who is helping whom? Student concerns about AI-teacher collaboration in higher education classrooms. Proceedings of the ACM on Human-Computer Interaction, 9(2), 1–32.https://doi.org/10.1145/3711104
Hooshyar, D., Tammets, K., Ley, T., Aus, K., & Kollom, K. (2023). Learning analytics in supporting student agency: A systematic review. Sustainability, 15(18), 13662.https://doi.org/10.3390/su151813662
Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20(1). https://doi.org/10.1007/s40979-024-00149-4
Juelskjær, M., & Schwennesen, N. (2012). Intra-active Entanglements – an interview with Karen Barad. Kvinder, Koen Og Forskning. https://doi.org/10.7146/KKF.V0I1-2.28068
Kholid, M. N., Hendriyanto, A., Sahara, S., Muhaimin, L. H., Juandi, D., Sujadi, I., Kuncoro, K. S., & Adnan, M. (2023). A systematic literature review of Technological, Pedagogical and Content Knowledge (TPACK) in mathematics education: Future challenges for educational practice and research. Cogent Education, 10(2). https://doi.org/10.1080/2331186x.2023.2269047
Kleinman, A., & Barad, K. (2012). Intra-actions. Mousse Magazine, 34(13), 76–81.
Knox, J. (2023). AI and education in China: Imagining the future, excavating the past. Routledge.
Krause, S., Panchal, B. H., & Ubhe, N. (2025). Evolution of learning: Assessing the transformative impact of Generative AI on higher education. Frontiers of Digital Education, 2(2), 1–15.https://doi.org/10.48550/arXiv.2404.10551
Krippendorff, K. (2011). Computing Krippendorff’s alpha-reliability. https://repository.upenn.edu/handle/20.500.14332/2089
Krippendorff, K. (2018). Content analysis: An introduction to its methodology. https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=8ZyYMGgAAAAJ:5MTHONV0fEkC
Kvale, S. (1996). InterViews: an introduction to qualitive research interviewing.
Lan, M., & Zhou, X. (2025). A qualitative systematic review on AI empowered self-regulated learning in higher education. Npj Science of Learning, 10(1), 21.https://doi.org/10.1038/s41539-025-00319-0
Lee, S., & Park, G. (2024). Development and validation of ChatGPT literacy scale. Current Psychology (New Brunswick, N.J.), 43(21), 18992–19004.https://doi.org/10.1007/s12144-024-05723-0
Lin, H., & Chen, Q. (2024). Artificial intelligence (AI) -integrated educational applications and college students’ creativity and academic emotions: students and teachers' perceptions and attitudes. BMC Psychology, 12(1), 487.https://doi.org/10.1186/s40359-024-01979-0
Liu, W. (2021). Does teacher immediacy affect students? A systematic review of the association between teacher verbal and non-verbal immediacy and student motivation. Frontiers in Psychology, 12, 713978.https://doi.org/10.3389/fpsyg.2021.713978
Lunde, C., Burén, J., Reinholdsson, T., & Daneback, K. (2023). Sexting experiences and motivations among adolescents with ADHD and ASD. Computers in Human Behavior, 140(107595), 107595.https://doi.org/10.1016/j.chb.2022.107595
Luo, J., Zheng, C., Yin, J., & Teo, H. H. (2025). Design and assessment of AI-based learning tools in higher education: a systematic review. International Journal of Educational Technology in Higher Education, 22(1). https://doi.org/10.1186/s41239-025-00540-2
Marrone, R., Zamecnik, A., Joksimovic, S., Johnson, J., & De Laat, M. (2025). Understanding student perceptions of artificial intelligence as a teammate. Technology Knowledge and Learning, 30(3), 1847–1869.https://psycnet.apa.org/doi/10.1007/s10758-024-09780-z
Mattar, J., Santos, C., & Cuque, L. M. (2022). Analysis and comparison of international digital competence frameworks for education. Neveléstudomány. https://doi.org/10.3390/educsci12120932
Maxwell, J. (2009). Designing a Qualitative Study. In The SAGE Handbook of Applied Social Research Methods (pp. 214–253). SAGE Publications, Inc.
Murris, K. (2022). Karen Barad as educator: Agential realism and education (2022nd ed.). Springer.
Nazaretsky, T., Mejia-Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2024). AI or human? Evaluating student feedback perceptions in higher education. In Lecture Notes in Computer Science (pp. 284–298). Springer Nature Switzerland.
Nemorin, S. (2024). Towards decolonising the ethics of AI in education. Globalisation Societies and Education, 1–13.https://doi.org/10.1080/14767724.2024.2333821
Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability. https://doi.org/10.3390/su16030978
Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. https://books.google.com/books?hl=es&lr=&id=ovAkBQAAQBAJ&oi=fnd&pg=PP1&dq=Qualitative+Research+%26+Evaluation+Methods.&ots=ZS-V3sxCA_&sig=fqKdTxOn_J4fLiZuJzxjhegoaZ4
Peláez-Sánchez, I. C., Velarde-Camaqui, D., & Glasserman-Morales, L. D. (2024). The impact of large language models on higher education: exploring the connection between AI and Education 4.0. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1392091
Ponce Rojo, A., Fontaines-Ruiz, T., Bracho, A. S., & Cánquiz Rincón, L. (2025). From digital natives to AI natives: Emerging competencies and media and information literacy in higher education. Education Sciences, 15(9), 1134.https://doi.org/10.3390/educsci15091134
Rakisheva, A., & Witt, A. (2023). Digital competence frameworks in teacher education-A literature review. Issues and Trends in Learning Technologies, 11(1). https://journals.librarypublishing.arizona.edu/itlt/article/id/5205/
Ren, X., & Wu, M. L. (2025). Examining teaching competencies and challenges while integrating artificial intelligence in higher education. TechTrends : For Leaders in Education & Training, 69(3), 519–538.https://doi.org/10.1007/s11528-025-01055-3
Rosiek, J. L. (2018). Agential realism and educational ethnography: Guidance for application from Karen barad’s new materialism and Charles sanders peirce's material semiotics. In The Wiley Handbook of Ethnography of Education (pp. 403–421). John Wiley & Sons, Inc.
Shi, L. (2025). Assessing teachers’ generative artificial intelligence competencies: instrument development and validation. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13684-5
Swacha, J., & Gracel, M. (2025). Retrieval-Augmented Generation (RAG) chatbots for education: A survey of applications. Applied Sciences (Basel, Switzerland), 15(8), 4234.https://doi.org/10.3390/app15084234
Thomson, S. R., Pickard-Jones, B. A., Baines, S., & Otermans, P. C. J. (2024). The impact of AI on education and careers: What do students think? Frontiers in Artificial Intelligence, 7, 1457299.https://doi.org/10.3389/frai.2024.1457299
Tierney, A., Peasey, P., & Gould, J. (2024). Student perceptions on the impact of AI on their teaching and learning experiences in higher education. Research and Practice in Technology Enhanced Learning, 20, 005.https://doi.org/10.58459/rptel.2025.20005
Tóth, B., Berek, L., Gulácsi, L., Péntek, M., & Zrubka, Z. (2024). Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed. Systematic Reviews, 13(1), 174.https://doi.org/10.1186/s13643-024-02592-3
van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Hindriks, S., Tummers, L., & Oberski, D. L. (2021). An open source machine learning framework for efficient and transparent systematic reviews. Nature Machine Intelligence, 3(2), 125–133.https://doi.org/10.1038/s42256-020-00287-7
Venter, J., Coetzee, S. A., & Schmulian, A. (2025). Exploring the use of artificial intelligence (AI) in the delivery of effective feedback. Assessment and Evaluation in Higher Education, 50(4), 516–536. https://doi.org/10.1080/02602938.2024.2415649
Viberg, O., Cukurova, M., Feldman-Maggor, Y., Alexandron, G., Shirai, S., Kanemune, S., Wasson, B., Tømte, C., Spikol, D., Milrad, M., Coelho, R., & Kizilcec, R. F. (2024). What explains teachers’ trust in AI in education across six countries? International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00433-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. Humanities & Social Sciences Communications, 12(1), 1–21. https://doi.org/10.1057/s41599-025-04787-y
Wang, X.-M., Wang, J.-L., Xu, S.-Y., & Xu, S.-J. (2025). Concept mapping in STEM education: a meta-analysis of its impact on students’ achievement (2004–2023). International Journal of STEM Education, 12(1). https://doi.org/10.1186/s40594-025-00554-2
Weidlich, J., Fink, A., Frey, A., Jivet, I., Gombert, S., Menzel, L., Giorgashvili, T., Yau, J., & Drachsler, H. (2025). Highly informative feedback using learning analytics: how feedback literacy moderates student perceptions of feedback. International Journal of Educational Technology in Higher Education, 22(1). https://doi.org/10.1186/s41239-025-00539-9
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235.https://doi.org/10.1080/17439884.2020.1798995
Wu, E. H.-K., Lin, C.-H., Ou, Y.-Y., Liu, C.-Z., Wang, W.-K., & Chao, C.-Y. (2020). Advantages and constraints of a hybrid model K-12 E-learning assistant chatbot. IEEE Access: Practical Innovations, Open Solutions, 8, 77788–77801.https://doi.org/10.1109/ACCESS.2020.2988252
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0171-0
Zewei, Tian, Liu, A., Esbenshade, L., Sarkar, S., Zhang, Z., He, K., & Sun, M. (2025). Implementation considerations for automated AI grading of student work. In arXiv [cs.HC]. arXiv. http://arxiv.org/abs/2506.07955
Zhou, M., & Peng, S. (2025). The usage of AI in teaching and students’ creativity: The mediating role of learning engagement and the moderating role of AI literacy. Behavioral Science, 15. https://doi.org/10.3390/bs15050587
.png)
