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4 - 4:50 p.m. (3:30 -4:20 p.m. ADT)

The Learning Experience

Human Inquiry, Artificial Intelligence & Asking “Why?”: Rediscovering Our Purpose in Teaching and Learning

Abstract: As AI reshapes the edges of teaching and learning, this session returns to a foundational question: Why? Drawing on two decades of practice, we frame AI not as a threat but as a clarifying force that helps educators protect human judgment, creativity, belonging, and ethical reasoning. Participants will explore a practical Three Horizons model—AI as assistant, thinking partner, and co‑creator—alongside strategies for redesigning assignments, strengthening provenance, and assessing process over product. The focus is on aligning purpose, practice, and AI use so learning becomes more meaningful, transferable, and human‑centered.

Summary: As artificial intelligence increasingly handles surface‑level academic tasks, higher education is confronted with a fundamental question: What is learning for? This session aligns with the conference theme Teaching for Tomorrow: Building Transferable Skills and Lifelong Learners by arguing that purpose—not technology—must anchor course and assignment design. Rather than framing AI as a threat, we position it as a clarifying force that pushes educators to foreground enduring, transferable human capacities: judgment under uncertainty, creativity, collaboration, belonging, and ethical reasoning. Drawing on evidence from learning theory and higher‑education research, the session introduces the Three Horizons model (Sharpe et al., 2016) as a practical framework for ethical AI integration: AI as assistant for routine tasks, thinking partner to augment inquiry, and co‑creator when students retain agency and responsibility. This staged approach helps instructors prevent students from bypassing learning while still leveraging AI to support deeper engagement. Research indicates that AI’s impact on learning is mediated by teaching methods rather than tools alone, reinforcing the need for intentional pedagogical design (Dong et al., 2026).

The session appeals across disciplines by focusing on universal academic practices rather than field‑specific content. Every discipline requires students to reason with incomplete information, apply methods responsibly, collaborate ethically, and use feedback productively. Participants will explore a process‑centered assessment approach—including role rotations, co‑creation, public audiences, evidence trails, and reflective memos—that aligns with authentic assessment, formative feedback, collaborative learning, and metacognitive reflection (Anderson & Krathwohl, 2001).

A key contribution is a language shift from “cheating” to “bypassing learning.” This reframing reduces shame, restores student agency, and clarifies what is lost educationally when AI substitutes for thinking. The framing supports clearer policies, healthier classroom climates, and consistent expectations across institutions.

By the end of the session, participants will be able to:

• Identify the human capacities their courses should prioritize for long‑term transfer.
• Apply the Three Horizons model to make ethical, intentional AI design choices.
• Redesign an assignment to assess process, reasoning, and provenance, not just output.

Participants will engage through brief reflective prompts, small‑group assignment redesign (“assignment surgery”), and peer feedback. Interactive polling and shared design templates ensure practical takeaways applicable across disciplines and institutional contexts.

Presenter(s):Dr. Stacey MacKinnon, Professor, Psychology

Affiliation: University of Prince Edward Island

Session Format: Presentation

Designing assignments that reduce student reliance on AI

Generative AI has prompted many instructors to reconsider how assignments support student learning. This session explores how the Transparency in Learning and Teaching (TILT) framework can help instructors increase students' understanding of why they are completing an assignment, what is expected of them, and how the work contributes to their learning, helping to reduce students' reliance on AI. Participants will explore practical examples and assignment redesign strategies that can be adapted across disciplines.

Presenter(s): Carolyn Best, Educator Developer, CITL

Affiliation: ����APP

Session Format: Presentation

SoTL and AI-Accelerated Research

Research in the age of Agentic AI : Understand, Adapt, Evolve

AI is doing much more than simply introducing new tools to university research. It is also beginning to influence how questions are formulated, how knowledge is explored, how evidence is interpreted and how research is communicated.
How should researchers respond to these changes? In which areas can generative and agentic AI genuinely support research, and in which areas is caution, critical judgement and human expertise essential?

This presentation invites you to explore the transformations that are already taking place throughout the research process, and to consider how these can evolve while maintaining rigour, accountability, and the vital role of human judgement.

Presenter(s): Florin Filip, Senior Advisor for Innovation, Creativity and Organizational Transformation

Affiliation: Universite de Moncton

Session Format: Presentation

Workshop

Think Before You Bot: Blueprinting Better Bots

Have you considered creating an AI assistant for tasks that are repetitive, time-consuming, or frustrating? As AI assistants become easier to build, the challenge is no longer whether we can build them, but whether we are designing them well. In this session, we will look beyond the prompts you type and buttons you click, to examine an array of intentional design decisions that shape effective AI assistants. Using SyllaBot, Dalhousie’s new AI-assisted syllabus checker as a case example, we will explore a practical blueprinting framework that considers factors such as purpose, audience, tone, scope, inputs, outputs, guardrails, and human-in-the-loop components. Participants will apply the framework to begin a preliminary design plan for an AI assistant that makes a task easier or solves a problem for yourself, your team, or your students. This session is applicable to all AI platforms (e.g., CoPilot, ChatGPT) and types of AI assistants (e.g., chatbot, agent).

Presenter(s): Daniella Sieukaran (she/her) Senior Educational Developer (Program Development), Centre for Learning and Teaching

Affiliation: Dalhousie Univeristy

Session Format: Workshop