AI Conversation Tool Program Pilot

2026 HERDSA Abstract: Collaborative Professional Learning: Active Peer Engagement That Supports Meaningful AI-Enabled Teaching

This project aimed to build academic capability in designing AI‑enhanced learning activities by developing a structured professional learning program that intentionally supported enhancing academic practice using an integrated AI Conversation tool within the JCU learning management system (LMS).

As universities navigate accelerating technological change, academics require professional learning that supports not only technical skill development but also pedagogically grounded shifts in practice (Whittle & Ransom, 2024). Laurillard’s Conversational Framework (2002, 2012) provides a robust pedagogical model for designing learning with technology and has been repositioned as a relevant foundation for GenAI‑enhanced learning design (Bakharia & Corrin, 2025; Beale, 2025).

A four‑week cross‑disciplinary pilot program was implemented to model professional learning that fosters pedagogical change. Participants engaged in synchronous webinars, creator sessions and pair‑share discussions, complemented by asynchronous exploration of curated resources, shared academic exemplars and guided AI‑supported design activities.

A mixed‑methods evaluation captured quantitative metrics—registrations (N = 200), LMS exploration hours (N = 189), AI conversation tool examples shared (n = 38), AI activities (n = 153), and webinar attendance (n = 126)—alongside qualitative insights from discussion forums, interactive webinar polling and a post‑program survey.

Findings indicate increased confidence and capability among academics in designing AI‑supported learning activities. Participants reported reduced perceived technical complexity and valued the structured opportunities for peer‑supported experimentation and dialogue. The institutional repository of AI‑enhanced learning activities expanded from 130 examples in May 2025 to 310 in October 2025, demonstrating growth from exploration to creation.

The initiative provides a scalable model for professional learning that supports change in academic practice. By embedding pedagogical theory, collaborative inquiry and authentic design activities, the program exemplifies how professional learning can guide educators in adapting practice within rapidly evolving AI‑mediated environments.

Hodgins C 1, Paerata T 2, Nutting T 2, Corbett N 2, Francis J 2
1 Central Queensland University, Rockhampton Qld, Australia
2 James Cook University, Townsville Qld, Australia

References

• Bakharia, A., & Corrin, L. (2025). Revisiting Laurillard’s conversational framework in the GenAI era. ASCILITE 2025 Conference Proceedings.
  ASCILITE publication page
• Beale, R. (2025). Dialogic pedagogy for large language models: Aligning conversational AI with proven theories of learning. arXiv.
  arXiv paper
• Belkina, M., et al. (2025). Implementing generative AI (GenAI) in higher education: A systematic review of case studies using LCF, SAMR and TPACK frameworks. Computers and Education: Artificial Intelligence.
  ScienceDirect article
• Diana Laurillard (2012). Rethinking university teaching: A conversational framework for the effective use of learning technologies (2nd ed.). Routledge.
  Routledge book page
• Whittle, R., & Ransom, J. (2024). Universities and AI: Developing new models of teaching and learning in the realm of radical uncertainty. N-TUTORR.
  N-TUTORR publication page

Digital Poster

In support of the abstract above, the following poster will be prepared as a 3-minute video discussion for the HERDSA 2026 Conference.

The evaluation feedback shows the pilot program was successful in building confidence and driving real use, with participants highly engaged and quickly moving from exploration to applying the tool in their teaching. The program worked best when it used practical, authentic examples from JCU colleagues, supported by clear guidance and one to one help. Participants were strongly motivated by improving student outcomes, ethical AI use, and creating engaging, real world learning activities such as role play and simulations.

The cohort was diverse in experience and in parts felt overwhelmed early on due to information overload and unclear scaffolding, reinforcing the need for simpler entry points and clearer pathways. While the tool itself proved engaging and “sticky”, participants now need support not just in using it, but in designing effective learning with it, including shaping AI personas, aligning activities to learning outcomes, and addressing concerns around time, workload, student uptake, and academic integrity.

AICT Context & Program Design