Teaching and Learning with AI Conference

Individuals in higher education may find it difficult to formulate coherent approaches to AI, but such alignment can be even more challenging across diverse departmental contexts. In this session, presenters describe the development of shared frameworks for AI engagement within a Department of Writing and Rhetoric. The department chair describes the complexities of this large, multifaceted department, and four program directors describe their strategies toward programmatic AI engagement including workshops, principles statements, and pedagogical revision. The presenters not only describe specific collaborative activities but also posit a model for creating collaboratively-based strategic alignment grounded in collective values.

As generative AI becomes ubiquitous, ambiguity around acceptable student use undermines learning and assessment. This session presents a structured framework aligning Bloom’s Taxonomy (Revised) with a five-level AI Assessment Scale adapted from Perkins, Furze, et al. (2024). Instructors assign a clear AI Level to each assessment so students know exactly how and when AI may be used, with allowances intentionally increasing from foundational tasks (remember, understand) to higher-order work (analyze, evaluate, create). Participants will explore assignment examples that scaffold AI use, promote student accountability, and assess judgment and critique.

Over time electronic resources offered by traditional library vendors have been stable products with understandable enhancements. The rapid emergence and widespread consumer access to Generative Artificial Intelligence (Gen AI) tools has compelled vendors, libraries, and librarians to urgently address this significant innovation. Vendors began developing and implementing new in-product Gen AI features as librarians faced a steep learning curve to evaluate, assess, make decisions about, and develop instruction around Gen AI add-ons. This session will offer a framework and actionable steps to help librarians with the challenge of new Gen AI tools showing up inside trusted platforms.

Want practical ways to use GPTs in your teaching without turning learning into a shortcut? This 30-minute rapid-fire session tours a set of GPT-based classroom tools I use with undergraduate nursing students to build real skills and, just as importantly, spark ideas you can adapt in your own courses. You’ll see structured practice for interprofessional communication, guided health assessment, and NCLEX-style item generation with rationales students can analyze (not just memorize). I’ll also demo choose-your-own-adventure clinical scenarios with realistic consequences and a mystery pathopharmacology “decoder” case. The goal is transferable strategies with endless classroom possibilities. #PracticalAITools #HealthProfessionsEducation #ClinicalReasoning

Faculty, staff, and students hold vastly different perspectives on AI in education—from enthusiastic adoption to deep skepticism. This session explores practical strategies for creating psychologically safe environments where diverse stakeholders can discuss AI openly, address concerns authentically, and collaboratively develop policies and practices. Participants will learn facilitation techniques, conversation frameworks, and institutional approaches that move beyond polarization to productive dialogue. Whether you're leading a department meeting, faculty development session, or campus-wide initiative, you'll gain tools to navigate resistance, honor legitimate concerns, and build shared understanding around AI's role in teaching and learning.#productive-dialogue #faculty-development #change-management

This presentation outlines instructional sessions tailored to train medical librarians, graduate medical students, and teachers in the combined use of Large Language Models (LLMs) and Retrieval‑Augmented Generation (RAG) workflows to support the synthesis of verifiable evidence. Presenters will share structured demonstrations and prompt engineering techniques designed to teach the effective use of two distinct platforms:• Copilot: Generate efficient search strategies, integrating MeSH terms, the PICO framework, and Boolean operators.• NotebookLM:  Integrating JBI‑PRISMA scoping review steps to support transparent, framework‑aligned synthesis.Participants will leave with adaptable retrieval-aware prompt templates and AI‑enhanced synthesis strategies for their teaching and reference services.

Many educators are experimenting with AI in their teaching while still trying to understand what is helping, what is not, and why. This session introduces KEEP SHINING, a human-centered framework for making sense of teaching with AI. Using concrete examples from digital media, video, and social media assignments, participants will examine what has supported learning and connection, and what has not. The session helps educators refine existing practices or begin with greater clarity and confidence. This interactive session invites participants to reflect on their experiences with AI and leave with a concrete takeaway to support more human-centered teaching.#HumanCenteredAI #DigitalMedia

Microsoft can be fun, too! This session explores creative and often overlooked ways Microsoft Copilot can support teaching and learning without requiring a separate paid Copilot subscription. Participants will see how course content can be transformed into narrated videos, editable infographics, and engaging assets that help bring concepts to life for students. The session highlights approaches for building visual learning materials using institutionally provided Microsoft tools, keeping instructional work within secure, familiar, and supported platforms. Participants will leave with new ideas for enhancing teaching, learning, and student engagement while discovering additional capabilities that can support a variety of instructional goals.

AI is everywhere in higher education, but learning doesn’t automatically follow. Too often, AI shows up as a shortcut to answers rather than a support for thinking. This session explores what happens when AI is designed to be pedagogical. In Spring 2025, Miami Dade College partnered with Kyron Learning to embed learner centered AI into a high enrollment online English Composition course. Integrated directly into Canvas and aligned to instructor defined objectives, the AI guided students through dialogue, practice, and feedback while faculty retained full control. Results show higher engagement, confidence, preparedness, and deeper conceptual understanding.

This practice‑based study examines the implementation of an AI‑supported tutoring system (CircleIn) in undergraduate Mathematics and Biology courses to explore its influence on student engagement. Embedded within active‑learning assignments, the AI tutor was intentionally designed as a learning scaffold rather than a shortcut, supporting problem generation, immediate feedback, concept clarification, and peer collaboration. Using a mixed‑methods approach, the study analyzes AI usage metrics, course performance data, and anonymous student surveys. Preliminary findings suggest the AI tutor supports behavioral, cognitive, and emotional engagement, particularly for quiet students, and enhances understanding of complex STEM concepts.Keywords: Engagement; AI-supported Tutor

Can you tell the difference between a human and artificial intelligence? Join us for a group-based social activity where we put our determination skills to the test and find out if we can know the difference between the work of AI and the work of a human being.

In this session, attendees will participate in a series of activities where we work in groups, to conclude if presented text, images, videos, or sounds are the work of AI or a human. Attendees will be placed within groups of 5-10 and shown works of either AI or humans on a PowerPoint presentation. Attendees will then deliberate and provide their answer for each slide to the event organizers. After gathering all answers, the event organizers will state how many groups were able to correctly determine if the shown work was AI or human. At the end of the session, participants will be given time to discuss the event and their level of confidence with determining a work of AI and the work of a human.