Teaching and Learning with AI Conference

Students often overtrust or dismiss AI outputs, lacking the critical skills to assess model reliability, bias, and limitations. This session presents a framework that uses AI system cards, model cards, and technical reports to teach critical thinking. By analyzing how training data, design decisions, and ethical trade-offs shape AI behavior, students learn to evaluate outputs with informed skepticism, building trust through understanding and preparing for thoughtful, responsible collaboration with AI in academic and professional contexts.

Generative AI produces fluent and persuasive output that can obscure factual errors, bias, and misalignment with academic expectations. This session introduces two complementary, classroom-tested strategies for teaching students how to evaluate AI-generated content with purpose rather than relying on surface-level correctness checks. Drawing on an AI Output Evaluation Worksheet and the Triple-R strategy (Read, Relevance, Represent), participants will explore practical methods for embedding accuracy, relevance, ethical reflection, and accountability into assignments so students critically assess AI output before incorporating it into their academic work.

If the principal learning objective of a business ethics class is to enhance student ethical decision-making capabilities, how do AI platforms (e.g. ChatGPT, Gemini, Grok) both aid and hinder the achievement of this goal?   This session examines the promises and pitfalls around using AI software in a case discussion where the open-ended inquiry method is utilized. Using an example, while AI software greatly aids the information gathering and distillation process necessary for decision-making, it lacks any tangible sensitivity for what information matters, as well as a sensibility for the “right decision.”  Solutions are proposed to address this issue.KEYWORDS:  open ended case analysis, ethical decision-making

This 30-minute, practice-focused session demonstrates how faculty can use AI tools to support first-generation, multilingual, and neurodivergent students in college classrooms. Drawing from real teaching practices, the session showcases concrete strategies using tools such as Microsoft Immersive Reader, Otter.ai, MyStudyLife, AI captions in PowerPoint and Zoom, and Be My Eyes to support reading and writing, organization, focus, and classroom access. Participants will learn how to frame AI as academic support rather than a shortcut, with attention to transparency, ethics, and inclusive pedagogy. Attendees will leave with adaptable practices they can immediately apply across disciplines.#AIpedagogy #Accessibility #InclusiveTeaching

AI has presented a series of massive challenges in education, and especially in journalism. In this session, we will discuss strategies for using AI in journalism education, including classroom expectations and boundaries, AI's abilities and limitations, assignment ideas, class policies for academic and journalistic integrity, and other tools for encouraging students to use AI for good and not evil.#AI #journalism #education

This session examines the creation and implementation of the COBALT (College of Business + AI for Learning and Technology) Initiative, a faculty-centered approach to integrating AI into teaching and learning at scale. Rather than focusing on tools alone, COBALT was designed as an institutional framework supporting experimentation, shared language, and pedagogical alignment across disciplines. The session will explore how the initiative was developed, how faculty engagement was cultivated, early victories achieved, and critical lessons learned along the way. Emphasis will be placed on practical decisions, missteps, and strategies that other institutions can adapt to their own contexts. #FacultyLedAI #InstitutionalDesign #TeachingWithAI

As generative AI becomes embedded in higher education, AI hallucinations—plausible but unsupported outputs—pose a growing threat to academic integrity and student learning. This session examines how hallucinations manifest in academic work and why higher education is uniquely vulnerable to fluent but inaccurate AI-generated content. Emphasizing prevention over surveillance, the presentation explores strategies for grounding AI use in verified sources, requiring transparent uncertainty and citation practices, and maintaining human oversight in academic workflows. The session concludes by reframing academic integrity for AI-rich environments, arguing that the goal is not an AI-free classroom but a learning-centered one grounded in accuracy, verification, and intellectual responsibility.

GenAI makes knowledge freely accessible, rendering knowledge transmission-focused education obsolete. To remain relevant, higher education must orient toward whole-person development where students "actively engage in creating an experience unique and worthwhile" (Watkins, 2025). But how do we design for this? This session presents a replicable process using Fink’s (2013) Taxonomy of Significant Learning—a framework for designing learning objectives that relate knowledge to the learner and includes learning domains such as application, human connection, and self-discovery. Participants will gain actionable steps for curriculum adaptation that center learner agency, ensuring programs remain relevant for college students in the age of genAI.

What if STEM assessments felt less like chores and more like adventures? In this session, we share the playful, challenging, and deeply authentic activities we’ve built by blending engaging project-based learning with AI-embracing creativity. Students navigate escape-room lab practicals, co-author scientific storyworlds, participate in peer-reviewed demonstrations, and design artifacts that connect experiments across the semester. Faculty, meanwhile, use AI to prototype rubrics, brainstorm twists, and refine prompts. The result: assessments that students actually want to complete and that reveal what they truly understand. Do you accept this challenge? Embrace the chaos with us!