Teaching Exchange Fellowship Scheme Seminar – Effective Professional Development in Teaching and Learning: What Does It Look Like?

Event Details

Date : 26 Feb 2026 (Thu)

Time : 12:30pm – 1:30pm

Venue : Learning Lab (RRS 321, 3/F, Run Run Shaw Building, Main Campus, HKU)

Speaker : Dr. Jannie Roed, Director, TALIC, HKU

Facilitator : Prof. Luke Fryer, Assistant Director, TALIC, HKU

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Abstract

In late November 2025, Professor Catherine Bovill from the University of Edinburgh visited HKU. She is currently the external examiner for the Postgraduate Certificate in Academic Practice (PCAP). During her visit, she discussed the PCAP programme with past and current participants and we compared professional development offered at our two universities.

This sharing session will focus on the advantages and challenges around programmes such as the PCAP. It will explore ways in which HKU can support its teaching staff on a more continuous basis than is currently the case.

About the Speaker

Dr. Jannie Roed is coordinator for Advance HE accredited programmes at HKU. As part of this role, she facilitates training sessions for mentors and assessors on the HKU Advance HE Fellowship Scheme. She also co-facilitates workshops for academic staff working towards Associate Fellow and Fellow of Advance HE. In addition, Jannie contributes to the teaching on TALIC programmes for new teaching staff.

For information, please contact:

Ms. Canice MOK

Teaching and Learning Innovation Centre

Facilitating Two-way Feedback with AI-powered Feedback Analytics

Event Details

Date : 6 Feb 2026 (Fri) Time : 12:00pm – 1:00pm (HKT) Venue : Online (ZOOM) Chairman : Dr. Yi-Shan Tsai, Monash University Facilitator : Prof. Cecilia K.Y. Chan, CoP Chair; Professor, HKU
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Abstract

Feedback is crucial to successful learning. It serves to help learners make sense of their current progress and bridge it with desired goals. However, feedback is often underutilised in higher education for various reasons, including inconsistency in its quality and learners’ lack of ability to utilise feedback effectively – a capability known as feedback literacy. One prominent challenge to improving feedback effectiveness and supporting learner feedback literacy is the lack of mechanism to track learners’ engagement with feedback systematically. In this talk, I will introduce PolyFeed, a feedback analytics tool designed with higher education educators and learners, to enhance two-way feedback. I will explore insights trace data may reveal about feedback literacy, and implications for learning and teaching. I will also discuss how feedback literacy may be reconceptualised in the context of AI-mediated feedback processes.

Plan Master AI – A GPS for Lesson Design

A common challenge for pre-service teachers is the difficulty of integrating theory into practice, often resulting in discrepancies between their instructional ideas and actual classroom outcomes. Many pre-service teachers (PSTs) find themselves being “over-ambitious” setting objectives that are too difficult without providing appropriate scaffolding for their students. 

“Plan Master AI acts like a “GPS for Lesson Design”: while the teacher remains the driver of the classroom, the AI provides a reliable map that flags missed turns in logic and ensures the “educational vehicle” is properly equipped to reach every student, regardless of the terrain.” Prof. Elizabeth Loh from the Faculty of Education.

Nurture Future Teachers in Lesson Planning

Effective lesson planning is the cornerstone of professional teacher training, serving as the vital bridge that connects educational theory to classroom practice. To empower PSTs with the skills and confidence needed for this complex task, Professor Elizabeth K.Y. Loh and her team at the HKU Faculty of Education, in collaboration with Dr. Carson Hung and Mr. Marco Leung from TALIC, developed “Plan Master AI (PM AI)”. This TDLEG-funded project leverages Generative AI to nurture future educators in designing their lesson plans for classroom instructions.

Support Learning Differences

Students learn best when they feel their strengths, values and needs are supported. Plan Master AI was designed specifically to cater to learning diversity, i.e., including students with special educational needs (SEN), gifted learners, and non-native Chinese speakers. Using the advanced technology of Plan Master, pre-service teachers are well guided to address and analyze the learning needs and “pain points” of their students and help them set level-appropriate objectives in their teaching.

Forging EdTech Partnership

Names (left to right):

  • Prof. Lillian Luk, Assistant Professor, TALIC, HKU
  • Prof. Ka Yee Elizabeth Loh, Assistant Professor, Language and Literacy Education, Faculty of Education, HKU
  • Ms. Yiling Song, EdD Student, Language and Literacy Education, Faculty of Education, HKU
  • Dr. Carson Hung, Lecturer / E-learning Technologist, TALIC, HKU
  • Mr. Marco Kwan Lok Leung, IT Staff, TALIC, HKU

The project’s success is rooted in the close collaboration between the Faculty of Education and the technical expertise of TALIC. Together, they “plugged” the AI with EDB curriculum guides, specialized course content, and assessment rubrics. This ensures that the feedback provided by the platform is not only technically sound but also aligns with professional standards and HKU’s academic expectations.

How It Works: 13 Dimensions of Rigorous Design

The PM AI platform provides a structured environment where students fill in 13 core sections of a teaching plan, ranging from student background and teaching theory to detailed procedures and assessment methods 

  • Consistency and Alignment: The AI meticulously checks for logical alignment between teaching objectives, content, and the actual steps of the lesson.
  • Scaffolding and Logic: It verifies that teaching steps move from the “most basic” to the “more advanced,” ensuring students learn step-by-step.
  • Choice of LLMs: Students have the autonomy to choose from various high-performance models, including Llama 3.3, DeepSeek R1, Perplexity Sonar, and Grok 4, to receive different perspectives on their work.
  • Personalized Interaction: Beyond simple checks, the platform offers a Teaching Material Adjustment Model to help PSTs fine-tune the complexity of their content for diverse learners./li>

One critical insight from the developers is that the AI should “not be too smart”; the goal is to stimulate the PST’s thinking process and provide guidance rather than doing the work for them.

Impact: Boosting Confidence and Efficiency

The platform was rolled out to 73 pre-service teachers, and the results have been overwhelmingly positive:
  • 63.8%: “PM AI” enhances the quality of their lesson plans
  • 62%:  ““PM AI” drives teaching innovation”
Quote: A Year 5 student shared: “PM AI is a highly helpful assistant… it guarantees that no important elements and details are overlooked,” giving them the confidence that their work is of high quality.
A Year 5 student
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"PM AI is a highly helpful assistant... it guarantees that no important elements and details are overlooked," giving them the confidence that their work is of high quality.

Empower Our Future Teachers and Educators

Looking forward, the team is expanding Plan Master AI to tackle specific challenges in Classical Chinese education, which many PSTs find to be a “difficult mountain to climb. Future developments will also include multi-modal checking (such as analysing the logic of visual teaching flow diagrams) and expansion into English language teaching. 

Plan Master AI is not just a tool; it is a blueprint for how AI can empower the next generation of researchers and educators at HKU.

EdTech Consultation

Enhancing and Tracking Students’ Engagement and Learning in an AI era through a Novel LMS – Vox

Event Details

Date : 2 Feb 2026 (Mon)

Time : 12:30pm – 1:30pm

Venue : Learning Lab (RRS 321, 3/F, Run Run Shaw Building, Main Campus, HKU)

Speakers :

  • Dr. Elizabeth Barrett, Senior Lecturer, Human Communication, Learning, and Development, Faculty of Education, HKU
  • Dr. David Villena, Assistant Lecturer, School of Humanities, Faculty of Arts, HKU
  • Prof. Michael Botelho, Professor, Restorative Dental Sciences, Faculty of Dentistry, HKU

Facilitators :

  • Prof. Michael Botelho, Professor, Restorative Dental Sciences, Faculty of Dentistry, HKU
  • Dr. Carson Hung, Lecturer / E-learning Technologist, TALIC, HKU
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Abstract

While AI can make learning frictionless, meaningful education requires deliberate engagement—a “slow food” approach to foster deep, satisfying learning. This necessity calls for pedagogical tools and platforms designed to craft challenging and enlightening experiences.

This seminar explores how the innovative HKU LMS platform, Vox, transforms teaching and learning in class and online. It begins with an overview of Vox’s features for creating engaging learning journeys. Subsequently, three HKU educators from diverse disciplines (Education, Humanities, and Dentistry) will present case studies on using Vox to cultivate essential skills in the AI era. Their presentations will contextualize how the platform facilitates strategies such as the flipped classroom, peer review, video commentary, and collaborative group work.

Ideal for educators interested in practical technology integration, this session will offer strategies to foster deep learning, skills development, peer engagement, and reflective practice in professional education.

About the Speakers

Dr. Elizabeth Barrett
Dr. David Villena
Prof. Michael Botelho
For information, please contact:

Ms. Wing LIN

Teaching and Learning Innovation Centre

Unlocking the Potential of Feedback with Generative AI: Opportunities, Challenges, and Lessons from Practice

Event Details

Date : 23 Jan 2026 (Fri)

Time : 3:00pm – 4:00pm (HKT)

Venue : Online (ZOOM)

Chairman : Dr. Guanliang Chen, Monash University

Facilitator : Prof. Cecilia K.Y. Chan, CoP Chair; Professor, HKU

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Abstract

Generative AI (GenAI) is rapidly reshaping how feedback can be designed and delivered in education. This seminar examines how GenAI can support feedback practices by helping educators analyse and improve feedback quality, and by enabling more timely, personalised, and actionable feedback for students at scale. It also critically discusses key challenges and risks, including hallucination and misalignment with established feedback theories. Finally, the talk presents Edvance, a GenAI-powered feedback tool developed and evaluated at Monash University, illustrating how theory-informed and responsible uses of AI technologies can effectively support student learning.

Empowering Confident Clinical Communication using AI-Enhanced VR Simulation

Effective clinical handovers are vital for seamless patient care and strong teamwork in healthcare. 

To build essential skills, competence and confidence, Nursing students benefit greatly from realistic, interactive practice. The AI-Enhanced Virtual Reality Simulation System in Nursing practice, co-created by the innovative team led by Dr. Maggie Chan*, Dr. Benney Wong, Mr. Abraham Wan at the School of Nursing, and Dr. Carson Hung and Mr. Ziv Tai at TALIC, were designed to prepare nursing students for professional nursing communications through cutting edge simulation technology.

Advancing Skills Through Interactive Practice

Nursing students thrive when given opportunities for hands-on, contextual learning that mirrors real clinical environments. In the simulated program, nursing instructors were created as humanoid avatars, i.e. NPCs, who interact with the nursing students in the virtual clinical environment. AI technology is integrated into the nursing instructor avatars and virtual environment to provide realistic responses and feedback much like in real life clinical handover scenarios. These virtual simulations enable nursing students to safely cultivate robust communication skills in clinical handover scenarios such as using structured frameworks including ISBAR (Identification, Situation, Background, Assessment, Recommendation), in order that these skills can be applied to their real-life counterparts.

Co-Creating AI-Enhanced VR Simulations

Names (left to right): 

  • Mr. Wai Hin Wan, Assistant Lecturer, School of Nursing, Li Ka Shing Faculty of Medicine, HKU
  • Mr. Ziv Tai, TALIC, HKU
  • Dr. Carson Ka Shun Hung, Lecturer / E-learning Technologist, TALIC, HKU
  • Dr. Jannie Roed, Director, TALIC, HKU
  • Dr. Maggie Mee Kie Chan, Senior Lecturer, School of Nursing, PolyU
  • Dr. Benney Yiu Cheong Wong, Lecturer, School of Nursing, Li Ka Shing Faculty of Medicine, HKU
  • Mr. Pak Hin Lai, Student, School of Nursing, Li Ka Shing Faculty of Medicine, HKU
  • Mr. Kin Fung Chan, Student, School of Nursing, Li Ka Shing Faculty of Medicine, HKU

The project’s strength lies in its dual focus: cutting-edge technology and a thoughtful co-creation process. Strong collaboration between faculty from the School of Nursing, TALIC and artificial intelligence tools is what made the VR project a success. Realistic clinical scenarios aligned with professional standards designed by nursing educators were transformed with TALICs facilitation into an effective VR training tool, with strong feedback from students who give valuable insights on how best the tool can support their training in clinical handover.

Dr. Maggie Chan*School of Nursing, PolyU
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The TALIC team transforms our ideas into effective teaching tools using VR and AI.

How It Works: Immersive Training with Real-Time Feedback

A huge dimension of the responsive and realistic nature of the VR tool is built up by AI technology. By integrating AI capabilities such as ChatGPT and Azure Speech Studio, the simulation enables fluid, unscripted conversations and real-time adaptation to individual needs. 

Precisely how this AI technology is integrated comes down to specific input data and scenarios provided by nursing professionals and TALIC’s facilitation of AI technological use. The simulation immerses students in authentic clinical settings, such as a medical ward or Accident and Emergency Department (AED), tailored to their year level—Year 3 undergraduates handle simpler cases in 7 minutes, while Year 5 students tackle complex ones in 10-12 minutes. Participants are required to take care of a patient, including taking vital signs and providing nursing care according to the patient’s condition, and then deliver an ISBAR-structured clinical handover to an AI nurse afterwards. Powered by GPT-4o, the AI analyzes communication patterns, tone, and completeness in real-time, offering prompts for missing details and instant feedback. Post-session, students get an AI-generated report with scores, areas for improvement, and gamified elements like task-based points.

Dr Benney WongSchool of Nursing, HKU
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The AI Nurse provides instant correction on missing or unclear information, ensuring a comprehensive clinical handover.

What Do Students Say? Feedback and Confidence-Boosting

The simulation was rolled out to over 430 nursing students across HKU’s Bachelor and Master programmes, and feedback from this run has shown clear benefits to student’s clinical handover training. Pre- and post-tests show enhanced self-efficacy, while rubrics for ISBAR and qualitative responses reveal improved communication skills. Students report feeling more prepared for practicums. Qualitative feedback from nursing students also show enjoyment and positive feedback: “It was a surprisingly fun and innovative way to perform clinical simulations”

Nursing Student Participant
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I learned a lot, especially about how to report to colleagues.

A Blueprint for Future Professional Education

This AI-enhanced VR simulation demonstrates how collaborative, technology-integrated approaches can transform professional training at HKU. Looking ahead, exciting developments such as AI-driven character animation, dynamic facial expressions, and more natural voice synthesis will create an even richer and more immersive learning environment.

*Dr. Maggie Chan is the initiator of the project and is formerly affiliated with HKU.

EdTech Consultation