Four Transformations to Lean into as We Navigate GenAI in Higher Education

Generative artificial intelligence isn't simply disrupting higher education. It's challenging the foundations of what we believe about learning itself.

Over the past year at the Taylor Institute for Teaching and Learning, we've worked with thousands of educators navigating GenAI integration—through institutional working groups, research projects, workshops, research-informed frameworks, and communities of practice. What we're learning has less to do with technology adoption and more to do with a fundamental reckoning about: 

• What constitutes meaningful learning?
• How do we assess student learning authentically and with validity?
• Who are we as educators as we embrace this new reality?
• What are our ethical responsibilities to students and to our disciplines?

We are in a paradigm shift. And at the centre of it is a deceptively simple idea: the goal of human-GenAI collaboration is to augment, not replace, human intelligence. Chiu and Rospigliosi (2025) remind us that GenAI brings extraordinary processing power, personalization, and the ability to generate and synthesize vast amounts of information. However, it is we as humans who bring questions, creativity, empathy, and critical evaluative judgement. Those capacities must remain at the centre of everything we do. This reframing matters to all of us. Because keeping humanity at the centre of GenAI is not just about how we use these tools, it is about whose humanity we are talking about, whose ways of knowing, and whose knowledge systems we are willing to honour in the process. 

Higher education centres around transformation: transforming knowledge, transforming practice, transforming individuals, transforming organizations, transforming communities, and transforming society. The pressing question is: how will human-GenAI collaboration catalyze our ability to achieve this purpose?

We propose that ethical, responsible and innovative GenAI integration in higher education requires transformation across four interconnected dimensions.

1) Program-Level Transformation

Universities are too focused on individual instructors navigating GenAI in isolation in their courses. Meaningful change requires shifting attention to the academic program, department and faculty as the focus of change. This middle (or meso) level is so often a gap in organizational change processes (Kenny & Eaton, 2022; Trowler, et al., 2005).  We can start by asking foundations questions such as:

• What do students need to know and be able to do by graduation?
• How are we scaffolding and aligning teaching, learning, and assessment across years and courses to develop those capacities?
• How is GenAI shifting what students need to know and do throughout their program?
• What knowledge, skills and values for human-GenAI collaboration are essential in the discipline or field in terms of research, innovation and professional practice?

These conversations can't wait for cyclical curriculum reviews every five to seven years. The pace of change demands ongoing, embedded dialogue with educators, students, industry and professions, research leaders, and communities.

2) Assessment Transformation 

Assessment has an incredible influence on what, when, and how students learn (Gibbs & Simpson, 2005). Assessment transformation is not about tweaking individual assignments. It's about reimagining the entire assessment ecosystem. At UCalgary, we recently endorsed institutional principles for the assessment of student learning, co-created with academic leaders, educators, staff, and students. We are one of only five U15 institutions to do so.  As we reimagine assessment across higher education, we must consider the many factors that influence student assessment practices in academic courses, as well as across departments, institutions and society. 

Eaton et al. (2025) found that GenAI integration in assessment requires two key considerations: a) radical transparency about what constitutes acceptable GenAI use (see for example Perkins et al., 2024), and b) a focus on developing students' critical capacity to recognize the limitations of AI, and to prompt and evaluate AI outputs. Imagine students submitting not only their final work but a reflection on their prompting process: What did the GenAI get wrong? Where did it challenge their thinking? Where did they become the evaluator? What does this mean for their learning moving forward?

3) Belief Transformation

Although the risks of GenAI over-reliance, academic misconduct, and an erosion of independent thinking tend to dominate conversations, emerging research tells a more complex and nuanced story. When thoughtfully integrated, GenAI strengthens creative and critical thinking, reduces cognitive load and accelerates deep learning, improves learning and communication for students with disabilities, provides immediate feedback, supports personalized learning, reduces language barriers, and increases student motivation, interest and engagement (Nguyen et al., 2025; McGrath et al., 2025; Ma & Zhong, 2025; Walton et al., 2025; Zheng et al. 2025; Zhu et al., 2025). For educators, GenAI can reduce workload, support content curation, assessment, feedback, and lesson planning, and free attention for what matters most (Ma & Zhong, 2025). We can’t ignore these research-informed benefits. Our beliefs about teaching, learning, disciplinary expertise, and student success must continue to evolve based on research and honest reflection.

4) Relational Transformation

Too many GenAI conversations have leaned into enforcement and surveillance and are driven by fear and distrust rather than curiosity and partnership. The result, most often has been to revert to practices that are not grounded in research-informed teaching practices and what we know about how students learn best. What would it look like to re-centre our decisions on foundational values of academic integrity: honesty, trust, fairness, responsibility, courage, respect, reciprocity, and relationality (ICAI, 2021; Poitras Pratt & Gladue, 2022)?

Students are co-creators of learning, not recipients of it. When we respond to GenAI with restriction, we model exactly the opposite of what students need: how to use powerful tools wisely, ethically, and in community. 

How then, can we work with students as partners (Mercer-Mapstone et al., 2017) as we explore what human-GenAI collaboration could mean within the context of teaching, learning and assessment? How can we engage them thoughtfully in curriculum design, feedback and improvement processes? How can we partner with them to engage in and disseminate research that investigates GenAI in higher education and their disciplines?

Summary

These four transformations are a system. You cannot shift one without the others shifting in response.

The institutions that will thrive won't be those that banned GenAI fastest or adopted it most enthusiastically. They'll be those that used this moment to clarify their educational values and redesign around what humans do best: think critically in context, create meaningfully, and learn together.

A Call to Action

We invite educators, educational developers, academic leaders, staff members and students to identify one concrete action you can take this semester to advance transformation in your own context. Engage in a conversation with a colleague. Pilot a transparent GenAI reflection assignment or learning activity. Advocate for program-level dialogue about GenAI in your department. Use GenAI as a thought-partner on a project or idea. We’ll move forward, collectively into the future by exploring our beliefs, developing meaningful partnerships, and taking one deliberate action at a time.

Questions for Further Reflection

• In your own context, which of the four transformations feels most urgent and relevant? What transformation creates discomfort? What might that discomfort be telling you?
• What beliefs about learning shape your current assessment practices? Which of those beliefs still hold, and which are worth revisiting in the emergence of human-GenAI collaboration?
• What would it look like in your program or institution to work with students as partners in navigating the ethics and possibilities of human-GenAI collaboration?
• Where have you noticed fear or distrust driving GenAI decisions in your context? What would a curiosity-driven, relational response look like instead?

References:

Chiu, T.K.F and Rospigliosi, P. (2025). Encouraging human-AI collaboration in interactive learning environments. Interactive Learning Environments, 33(2). 921-924. https://doi.org/10.1080/10494820.2025.2471199

Eaton, S. E., Moya Figueroa, B. A., McDermott, B., Kumar, R., Brennan, R., & Wiens, J. (2025). What should we be assessing exactly? Higher education staff narratives on gen AI integration of assessment in a postplagiarism era. Assessment & Evaluation in Higher Education, 1–20. https://doi.org/10.1080/02602938.2025.2587246

Gibbs, G., & Simpson, C. (2005). Conditions under which assessment supports students’ learning. Learning and teaching in higher education, (1), 3-31.

International Center for Academic Integrity [ICAI]. (2021). The Fundamental Values of Academic Integrity. (3rd ed.). www.academicintegrity.org/the-fundamental-values- of-academic-integrity

Kenny, N., & Eaton, S. E. (2022). Academic integrity through a SoTL lens and 4M framework: An institutional self-study. In Academic integrity in Canada: An enduring and essential challenge (pp. 573-592). Cham: Springer International Publishing.

Ma, N., & Zhong, Z. (2025). A Meta‐Analysis of the Impact of Generative Artificial Intelligence on Learning Outcomes. Journal of Computer Assisted Learning, 41(5), e70117. https://doi.org/10.1111/jcal.70117

McGrath, C., Farazouli, A., & Cerratto-Pargman, T. (2025). Generative AI chatbots in higher education: A review of an emerging research area. Higher Education, 89(6), 1533–1549. https://doi.org/10.1007/s10734-024-01288-w

Mercer-Mapstone, L., Dvorakova, S. L., Matthews, K. E., Abbot, S., Cheng, B., Felten, P., ... & Swaim, K. (2017). A systematic literature review of students as partners in higher education. International Journal for Students as Partners.

Nguyen, K. V. (2025). The Use of Generative AI Tools in Higher Education: Ethical and Pedagogical Principles. Journal of Academic Ethics, 23(3), 1435–1455. https://doi.org/10.1007/s10805-025-09607-1

Poitras Pratt, Y., & Gladue, K. (2022). Re-defining academic integrity: Embracing Indigenous truths. In Academic integrity in Canada: An enduring and essential challenge (pp. 103-123). Cham: Springer International Publishing.

Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment. Journal of University Teaching and Learning Practice, 21(06). https://doi.org/10.53761/q3azde36

Trowler, P., Fanghanel, J., & Wareham, T. (2005). Freeing the chi of change: the Higher Education Academy and enhancing teaching and learning in higher education. Studies in Higher Education, 30(4), 427-444.

Walton, J., Bearman, M., Crawford, N., Tai, J., & Boud, D. (2025). How university students work on assessment tasks with generative artificial intelligence: Matters of judgement. Assessment & Evaluation in Higher Education, 1–17. https://doi.org/10.1080/02602938.2025.2570328

Zheng, Q., Yuan, X., & Xu, L. (2025). Impact of generative models on higher education: Exploring opportunities and challenges. Education and Information Technologies, 30(17), 25505–25542. https://doi.org/10.1007/s10639-025-13742-y

Zhu, Y., Liu, Q., & Zhao, L. (2025). Exploring the impact of generative artificial intelligence on students’ learning outcomes: A meta-analysis. Education and Information Technologies, 30(11), 16211–16239. https://doi.org/10.1007/s10639-025-13420-z

Note: This blog post was adapted from a panel presentation as part of a Special Plenary Navigating the Ethical and Safe Use of Artificial Intelligence in Post-Secondary Education, at the 2026 Peer Beyond Graduate Research Conference, University of Calgary (February, 19, 2026