Exploring GenAI Use in Teaching and Learning Enhancement
Introduction
The Redesigning Student Learning Experience in Higher Education (RSLEIHE) is an award scheme established in 2017 by the Hong Kong Branch of HERDSA (Higher Education Research and Development Society of Australasia). It aims to promote and showcase local examples of student-staff partnerships in teaching and learning, also known as Students as Partners (SaP; Cook-Sather et al., 2014). The scheme provides a platform that engages students and faculty in co-designing innovative learning experiences in response to emerging challenges in education, such as the adoption of educational technologies and pedagogical shifts during and after the pandemic. In the era of Artificial Intelligence (AI), the 5th RSLEIHE calls on students and faculty to collaborate in exploring best practices for integrating Generative AI (GenAI) tools to enhance teaching and learning.
Integrating GenAI into education presents “unparalleled opportunities and formidable challenges” (Bittle & El-Gayar, 2025, p.1). While GenAI offers opportunities to enrich student learning experience by enabling adaptive, responsive, and interactive learning process (Gupta et al., 2024, Bhatia et al., 2024), unintended consequences, such as unproductive cognitive offloading (Ahmedtelba, et al., 2025) and heightened risks of academic dishonesty (Bittle & El-Gayar, 2025), have made many educators hesitant to embrace it fully.
Fortunately, increasing evidence suggests that collaborative teaching practices involving teachers, students and AI tools can be effective. For example, in a study testing GenAI tools for creating reliable questions in various assessment activities at the University of British Columbia (Dehkhoda et al., 2024), the collaborative team of faculty and student partners found that an iterative, guided prompting approach was beneficial for developing question variations, while direct and explicit prompts were more effective for generating accurate solutions in a university Physics learning context. Results were verified by AI specialists.
Furthermore, Creely and Carabott (2025) in Australia established an Integrated AI-Oriented Pedagogical Model for reimagining future classroom practices and teacher education. The model outlines the educational implications of GenAI in three dimensions (Positionality of Teacher, Relationality in Pedagogy and Functionality of GenAI) and advocates for practices that respect human experience while embracing the agency of AI technologies.
Taken together, leveraging GenAI within a pedagogical partnership approach fosters collaborative exploration of AI-driven solutions. This is a fundamental shift in how universities approach teaching: moving away from traditional top-down instruction towards a collaborative teaching model, namely, the Trio model, where faculty, students and GenAI tools jointly reshape learning experiences.
Theme & Sub-themes
Main Theme
Exploring GenAI Use in Teaching and Learning Enhancement
Sub-themes
- Assessment and Feedback
- Curriculum Innovation and Programme Development
- Ethics and Integrity
- GenAI Tools and Pedagogy
- Self-regulated Learning
- Student AI Literacy and Digital Resilience
- Teacher AI Literacy and Digital Resilience
- Well-being and Resilience
Eligibility & Participation
A project team should consist of three key parties — students, teachers, and GenAI tools — forming a Trio team:
- Student members (2-4):
Full-time undergraduate and postgraduate students enrolled in local higher education institutions are eligible. Each team must include at least two students and no more than four students. - Teacher/staff members (1-2):
Full-time academic or professional staff members from local higher education institutes are eligible. Each team must include at least one and no more than two staff members. - GenAI tools (1-2):
Each project proposal must specify at least one and no more than two GenAI tools. Once a proposal is accepted, the team must continue using the chosen tools throughout the project. When selecting GenAI tools, teams must comply with their university’s policies and relevant regulations (such as GenAI use regulations, teaching and learning guidelines, assessment policies, ethical approval requirements). This ensures that AI use is ethical, responsible and free from risks or harms to participants. While all established GenAI tools are welcome, teams should avoid using tools that are still in the development stage to prevent unpredictable outcomes.
Cash Awards and Certificates
Awards |
Amount (HKD) |
|---|---|
| Distinguish Awards | |
| Champion | $7,000 |
| First Runner-up | $5,000 |
| Second Runner-up | $3,000 |
| Awards of Merits | $1,500 (each team) |
| People’s Choice Award | $1,000 |
Distinguished Awards will be judged based on the quality of written report and team presentation in the RSLEIHE Symposium on 27 February 2027.
People’s Choice Award (HKD 1,000) will be presented to the team receiving the highest number of public votes during the Symposium.
Participation Certificates will be awarded to all teams to acknowledge the completion of Trio projects.
Online Briefing Session
Date: 18 March 2026 (Wednesday)
Time : 1:00pm – 2:00pm
Venue : Zoom
Presentation File by Dr. Peter Lau
Timeline
2026
Date |
Core Activities |
Other Events |
|---|---|---|
| 2 March – 20 April |
|
|
| 18 March (Wednesday) 1:00pm – 2:00pm |
|
|
| April – Mid-May |
|
|
| 6 June (Saturday, whole-day) | Design Sprint | |
| July – August |
|
Consultation on SaP, GenAI, idea development |
| September – December |
|
Seminar series |
| December |
|
|
| 29 December (Tuesday) |
|
Pitch video submission |
2027
Date |
Core Activities |
Other Events |
|---|---|---|
| January – February |
|
Contribute reflective pieces to TLTHE special issue in May 2027 (TBC) |
| 27 February (Saturday) (Venue to be confirmed) |
|
|
| March |
|
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| 28 March |
|
|
| April – June |
|
Proposal Submission
Proposal
The project proposal should address following sections. The total length should not exceed 1,000 words (approximately 200 words per section). Proposals must be submitted through the online proposal submission form by Monday, 20 April 2026.
- Rationale, Purpose and Expected Outcomes
- Identify the specific challenges in Hong Kong higher education that the project seeks to address.
- Outline the planned activities and the expected outcomes briefly.
- Partnership
- Define the roles of students, teachers, and GenAI tools within the Trio team.
- Specify the partnership model or guiding principles for effective collaboration.
- Implementation
- Explain how the project will be carried out (e.g., piloting in a specific course or programme).
- Detail the steps and/or project phases involved.
- Project execution and evaluation should take place between September and December 2026 (see Timeline for details)
- Evaluation
- Describe what and how evidence will be collected to demonstrate the project’s success and impact.
- Ensuring success
- Identify potential challenges that may hinder success.
- Explain strategies to prevent or resolve these issues.
Selection Criteria
The submitted proposals will be reviewed based on the following criteria:
- Evidence of needs and benefits
- Potential impact
- Quality of design and feasibility (e.g., sound methodology or theorical framework)
- Sustainability
Language
The proposal must be written in English.
CoP Membership
Once a proposal is accepted and shortlisted, all the team members will be granted Apprentice Partitioner membership in the “Community of Practice for Student Partnership“. The CoP will serve as a network for cross-institutional collaboration.
Written Report, Presentation and Pitch Video
Project report should be submitted by 29 December 2026. Report writing guideline and template will be provided in due course. Major contents of the report include:
- Objectives
- Methodology/Process
- Evaluation, Results and Findings (should be evidence-based)
- Project Impact (Learning and Reflection)
- Implications and Recommendations for Teaching & Learning in Higher Education
Based on the report, the team will undertake an 8-minute presentation and 2-minute Q&A session in the RSLEIHE Symposium to be held on Saturday, 27 February 2027. Only student team members are expected to take part in the presentation. Guidelines for presentation will be available in due course.
Inquiry
For enquires:
Dr. Peter LAU
Teaching and Learning Innovation Centre
The Lead and Partner Institutions
The Lead Institution
Partner Institutions (in alphabetical order)
Higher Education Research and Development Society of Australasia
Hong Kong Baptist University
The Chinese University of Hong Kong
The Hong Kong Polytechnic University
The Hong Kong University of Science and TechnologyReferences & Selected Resources
References
- Ahmedtelba, A., Elycheikh, M., Mitereva, S., & Ponce, M. (2025). Critical integration of generative AI in higher education: Cognitive, pedagogical, and ethical perspectives. London Journal of Research in Humanities and Social Sciences, 25(13), 45–62.
- Bittle, K., & El-Gayar, O. (2025). Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4), 296. https://doi.org/10.3390/info16040296
- Bhatia, A., Bhatia, P., & Sood, D. (2024). Leveraging AI to transform online higher education: Focusing on personalized learning, assessment, and student engagement. International Journal of Management and Humanities, 11(1). https://www.ijmh.org/wp-content/uploads/papers/v11i1/A175311010924.pdf
- Creely, E., Carabott, K. (2025). Teaching and learning with AI: An Integrated AI-Oriented Pedagogical Model. Australian Educational Researcher, 52, 4633–4654. https://doi.org/10.1007/s13384-025-00913-6
- Dehkhoda, A. M., Kipp, D., & Chow, C. F. (2024). Transformative pedagogy: Leveraging generative AI tools for enhanced learning experiences. Proceedings of the Canadian Engineering Education Association (CEEA).
- Gupta, S., Dharamshi, R. R., & Kakde, V. (2024, February). An impactful and revolutionized educational ecosystem using generative ai to assist and assess the teaching and learning benefits, fostering the post-pandemic requirements. In 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) (pp. 1-4). IEEE.
Selected resources
AI in education
- Bittle, K., & El-Gayar, O. (2025). Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda. Information, 16(4), 296. https://doi.org/10.3390/info16040296
- Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges. Multimodal Technologies and Interaction, 9(8), 84. https://doi.org/10.3390/mti9080084
- Lan, Y.J., & Chen, N.S. (2024). Teachers’ agency in the era of LLM and generative AI: Designing pedagogical AI agents. Educational Technology & Society, 27(1), I–XVIII
- Tsao, J., & Wong, A. S. M. (2025). Guidebook: Generative Artificial Intelligence for Teaching and Learning. HKU Common Core. https://commoncore.hku.hk/wp-content/uploads/documents/Guidebook%20-%20GenAI%20in%20Teaching%20and%20Learning.pdf
Students as Partners
- Cook-Sather, A., Bovill, C., and Felten, P. (2014). Engaging students as partners in learning and teaching: a guide for faculty. San Francisco: Jossey-Bass.
- Matthews, K. E. (2016). Students as partners as the future of student engagement. Student Engagement in Higher Education Journal, 1(1) 1-5. Retrieved from https://journals.gre.ac.uk/index.php/raise
Scholarship of Teaching and Learning for Undergraduate Researchers
- Leung, L., & Zou, T. (2023). Guidelines on conducting the Scholarship of Teaching and Learning for undergraduate researchers. Department of Educational Administration and Policy, Faculty of Education, The Chinese University of Hong Kong. Retrieved from https://drive.google.com/file/d/15tTn4EdpJgjFDrL9ADYaZ4leeizxEHNa/view
Research Consultation Chatbot
- Edcafe Research Assistant (Chatbot): https://app.edcafe.ai/chatbots/6895c3a35c6ec4806ca5e032
