Teaching and Learning with AI Conference

ChatGPT changed the landscape of Higher Education, but was it avoidable? Historical context in Higher Ed’s adoption of past technologies can inform where we’ve gone wrong in the last 60 years, and where we’ve gone right. AI may not have started the fire, but it certainly put a microscope to the issues of Higher Ed. Ones that if we look closely enough can teach us how to adequately adapt and make the next AI-pocalypse or technological shift easier. Generative AI may have a lot to answer for - but maybe not all that we blame it for.

Understanding the intersection between AI and culture is important. Culture shapes the development and usage of AI, and it should be central in discussions about AI ethics, but is rarely targeted by guardrails. The lens of AI as a cultural artifact addresses large-scale questions about data sovereignty, language preservation, and worldviews represented in training data, as well as more practical questions about how to build AI fluency and how to frame AI for multicultural student populations. Attendees will learn to critically analyze AI through a cultural lens, and they will leave with practical activities for engaging students in culturally-centered AI.

As artificial intelligence becomes increasingly embedded in education, faculty must move beyond awareness toward purposeful, ethical, and inclusive classroom application. This session introduces an “AI Toolbox” approach that helps instructors select, evaluate, and deploy AI tools to support diverse learners, instructional goals, and assessment strategies. Participants will explore examples of AI use for content creation, accessibility, formative feedback, and student engagement while maintaining academic integrity and pedagogical rigor.#AIinEducation #FacultyDevelopment #InclusiveTeaching

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

We know that thoughtfully created educational games can increase student engagement and knowledge retention. Learn how to create engaging educational games without paying for new Generative AI tools or learning how to code. This talk shares rapid, responsible workflows for using AI to generate free games from start to finish, using and "AI draft → human verify → AI complete → deploy" workflow. You’ll see 3 ready-to-use formats (e.g., Jeopardy boards) plus copy/paste prompt templates to help you build games in your discipline. 

Beginning teachers often rely on reactive, directive responses when faced with challenging student behavior, limiting opportunities for long-term learning and reflection. This session introduces Edustories, a research-based approach that uses AI-supported analysis of authentic teaching case narratives to foster professional judgment. By engaging with AI-assisted reflections on classroom decision-making, teachers learn to recognize patterns in their responses, explore proactive alternatives, and strengthen their capacity to address complex behavioral situations more thoughtfully.#ai-in-education #teacher-learning #classroom-behavior

This session introduces a three-tiered strategy for AI-conscious teaching in higher education, emphasizing a humanistic and holistic approach. The approach focuses on establishing clear and transparent expectations for AI use by students, designing learning activities that prioritize human judgment, creativity, and voice, and integrating AI tools in ways that align with disciplinary goals and professional practice. Participants will leave with practical strategies for approaching AI use in their own teaching practices and among their students.