Teaching and Learning with AI Conference

Metaphors help us understand what’s new in terms of what’s familiar, so it’s no surprise that we talk about AI as a tool, assistant, collaborator, and tutor. This session argues that metaphors, while helpful, can also shape our thinking in ways that reduce agency, obscure moral responsibility, and undermine our critical capacities. Using examples from art and design education, where AI is sometimes considered a threat, we explore linguistic strategies that can restore ethical clarity, strengthen student accountability, and support faculty in creating assignments that foster critical engagement with AI, not passive trust.#AI-ethics, #AI-literacy, #responsible-AI

While much work has been done at the course level to create AI policy statements to govern individual courses, there is an increasing need for AI mission statements at the institutional level that are both flexible and reflective of the unique purpose and values of liberal arts education. Further, although there are a number of AI-focused centers at larger R1 institutions, I argue that liberal arts institutions are uniquely positioned to offer critical AI studies given their commitment to holistic learning, ethical reasoning, and critical thinking. Yet, such course work should be informed by the AI mission statements of the college. This session will be a bridge between work being done in classrooms and larger, institutional conversations about the role of AI on campus. As such, it will help guide faculty leaders in these conversations by providing a framework for crafting an institutional mission statement for AI that takes into consideration the needs of faculty, staff, and students. Specifically, this presentation will offer a method for composing, assessing, and refining an institutional AI mission statement based on work done at my institution.

As artificial intelligence becomes more accessible across higher education, health sciences faculty face a familiar tension: how to preserve time-tested teaching practices while adopting emerging tools that can strengthen learning, assessment, and faculty workflow. This presentation describes how faculty used BoodleBox—an education-focused platform that consolidates multiple AI applications—to enhance teaching and learning in health sciences contexts. Drawing on faculty-led use cases, the session highlights practical applications aligned with traditional instructional priorities: clear learning outcomes, sound assessment design, consistent rubrics, authentic case-based learning, and supportive feedback practices. Examples include developing case studies and class activities, refining assessments and rubrics, and streamlining course design tasks while maintaining academic integrity and disciplinary standards. Participants will leave with a set of adaptable strategies, implementation considerations, and discussion prompts they can use to evaluate fit, set guardrails, and support responsible adoption of AI in health sciences education—bridging the best of established pedagogy with purposeful innovation.

How can students use AI tools without letting AI do the thinking for them? Duke University’s Academic Resource Center developed an AI Toolkit that applies a “brain-first” framework to guide generative AI use in learning. This approach helps students use AI to support rather than shortcut their learning. Participants will experiment with example prompts for common learning challenges and consider how AI can reinforce essential cognitive work such as retrieval, interleaving, and metacognition. Participants will leave with adaptable examples that can be implemented in their own courses or learning support programs across diverse institutional contexts and disciplines (#effective-prompting, #metacognition, #AI-in-study-cycle).

AI is increasingly embedded in academic work, yet faculty expertise remains difficult to surface and support. This session shares a project that uses a structured dataset of real faculty decision-making to train a local AI model as a cognitive apprentice, supporting reflection and professional judgment rather than automating decisions. Participants will see how capturing decision context, constraints, and reasoning reveals patterns in faculty work and informs more thoughtful AI integration. The session includes interactive moments that invite participants to reflect on real faculty decision scenarios and how judgment shifts across contexts.

Adopting AI for productivity and operational efficiency makes sense, but neither of those address the core mission of higher education: engaged student learning. SchoolAI helps higher ed faculty engage today's students with active learning Spaces and LMS-informed virtual TAs, both in-class and online. We’ll showcase our new Canvas Informed Spaces that draw on course content and let you place a SchoolAI TA directly in the course navigation. We'll center on real examples of how college and university faculty are using AI to improve, not shortcut learning.

In 2025, USF Innovative Education Digital Learning, in partnership with University leadership and faculty, launched an AI-Augmented Course Pilot exploring how generative AI can be intentionally and responsibly integrated into course design to enhance learning and give students practical experience that supports future academic and professional readiness. Six criteria served as a framework to guide participating faculty in imbuing purposeful AI integration into courses. The session showcases the criteria, real examples of how they were met in 11 AI-augmented courses, and data-driven insights from stakeholders on the benefits and challenges of implementing meaningful, pedagogically sound AI integration across disciplines.

Abstract: Teamwork Transforms: U-M's Collaborative Leap Into Generative AI Abstract: In a rapidly evolving digital landscape, how does a decentralized institution unify to support AI literacy? This session explores the University of Michigan’s collaborative journey to integrate Generative AI across its 19 schools and colleges. We will detail how cross-functional instructional support groups formed the Teaching Technology Collaborative (TTC) to develop a comprehensive ecosystem of resources, including the U-M Instructor Guide, custom AI tools like UM-GPT, and a tiered workshop series. #incorporatingAI  #TeachingandLearningwith GenAI

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly transforming modern mining operations, including exploration, mine planning, automation, safety, and production optimization, creating a growing need for mining engineering curricula to equip students with relevant AI- and ML-related skills. This study investigates the effectiveness of a newly developed AI in the Mining Industry course in equipping mining engineering students with suitable AI and ML competencies and examines how students’ perspectives toward AI and ML applications in the mining industry change after completing the course. The course was developed in response to a prior comprehensive need-assessment study and was accordingly designed and implemented. To evaluate the course impact, the study employed the first two levels of the Kirkpatrick evaluation model, focusing on student learning outcomes and changes in student perspectives, using pre- and post-course surveys to assess self-reported competencies and perceived relevance of AI in mining. Preliminary findings indicate improvements in students’ understanding of AI and ML concepts within a mining context, along with a shift toward more informed and positive perceptions of AI’s role in the mining industry. This study addresses a gap in the literature on AI integration in Mining engineering education and provides evidence-based insights to support curriculum development aligned with emerging industry needs. Keywords: Artificial Intelligence, Machine Learning, Curriculum Evaluation, Mining Engineering Education

As AI tools reshape writing, many faculty find themselves, discouraged, scanning submissions, judging intentions, trapped in the role of the “AI Policing” instead of teaching. This session argues that the gradebook itself can become a way back to teaching writing: an opportunity to reframe assessment around human values, to restore trust, and to draw both faculty and students back to the page. Participants will learn how rebuilding rubrics around process, voice, creativity, and human judgement empowers instructors to uphold rigor, address AI misuse through grading (not surveillance), and refocus assessment on what truly counts: writing as thinking. #Assessment #AI and Writing Pedagogy #Process-Based Learning