Teaching and Learning with AI Conference

Faculty are spending more time policing AI submissions than designing meaningful assessments. The arms race is unwinnable, and detection tools keep producing false positives that damage real students. Oral assessment offers a different path. When students complete short oral responses on their own time, voice authentication confirms authorship and AI grading runs against the faculty member's rubric, not a vendor's. Aggregate response data even reveals where the teaching itself has a gap. The assessment doesn't just check integrity. It diagnoses the course. This session walks faculty through what changes when assessment becomes a conversation instead of an artifact.

In accelerated, blended humanities courses—particularly at STEM-focused institutions—students increasingly replace primary source reading with generative AI summaries, weakening comprehension and discussion. This session presents a practice-based teaching model that integrates GenAI as a structured reading-and-thinking scaffold rather than an integrity problem. Using scaffolded prompt-engineering assignments aligned with weekly primary sources, students engage GenAI in dialogic interpretation, comparison, and critical questioning. Participants will examine the assignment design, rubric, and coaching model, and leave with a transferable framework for ethically using AI to support reading-intensive courses without sacrificing rigor. #AI-integrated-pedagogy #prompt-engineering #academic-integrity-by-design

“Can I use AI for this?” is a question many of us hear daily, yet clear answers are still hard to find. In this session, a department director shares how her English faculty moved past AI bans and vague policies by developing the Be S.M.A.R.T. with AI framework. You’ll see how this model is reflected in assignment language, shared expectations, and "The Student Guide to AI" course learning module that helps students think through ethical use, effective prompting, creative thinking, and responsible decision-making. Leave with ideas you can adapt to bring transparency and consistency to AI use in your own teaching.

What are students missing when course content relies on visuals, audio, or interactions they cannot fully access? In this session, participants will experience accessibility from a student perspective through a live demonstration. We’ll explore how AI can generate audio descriptions and how instructors can refine them to ensure clarity and meaning. Framed around an “AI-assisted, human-powered” approach, this session highlights practical ways to design more inclusive, multimodal learning experiences that better support all students.

Your faculty know about AI. They've attended workshops. They have policies. So why hasn't your institution transformed? Two years of data revealed a stark truth: tactical training creates pockets of excellence surrounded by vast territories of uncertainty. This workshop shares the longitudinal evidence that exposed the engagement gap—and how it led to building a strategic AI center. Leave with a framework to assess whether your institution is truly ready for AI integration or stuck in perpetual transition. (institutional readiness, engagement gap, AI implementation)

What if there are no solutions (yet) to the questions AI pose to disciplinary learning environments?  Campus leadership and administrators face an impossible challenge: provide clear guidance, frameworks, and solutions, at scale, to the many dynamic challenges emerging AI technologies pose. Drawing on leadership experience from Ohio State University’s AI Fluency initiative, this session will demonstrate how a desire to find “solutions” paradoxically hindered progress by stifling the disciplinary inquiry necessary to address these continually evolving challenges. Session participants will explore leadership and pedagogical practices for resisting pressure to find solutions and instead create conditions where better questions emerge. What if there are no solutions (yet) to the questions AI pose to disciplinary learning environments?  Campus leadership and administrators face an impossible challenge: provide clear guidance, frameworks, and solutions, at scale, to the many dynamic challenges emerging AI technologies pose. Drawing on leadership experience from Ohio State University’s AI Fluency initiative, this session will demonstrate how a desire to find “solutions” paradoxically hindered progress by stifling the disciplinary inquiry necessary to address these continually evolving challenges. Session participants will explore leadership and pedagogical practices for resisting pressure to find solutions and instead create conditions where better questions emerge. What if there are no solutions (yet) to the questions AI pose to disciplinary learning environments?  Campus leadership and administrators face an impossible challenge: provide clear guidance, frameworks, and solutions, at scale, to the many dynamic challenges emerging AI technologies pose. Drawing on leadership experience from Ohio State University’s AI Fluency initiative, this session will demonstrate how a desire to find “solutions” paradoxically hindered progress by stifling the disciplinary inquiry necessary to address these continually evolving challenges. Session participants will explore leadership and pedagogical practices for resisting pressure to find solutions and instead create conditions where better questions emerge. 

As education moves toward integrating generative AI into assessments and learning, a critical question emerges: how do we ensure students are actually learning? This session explores how to design AI-integrated assessments that promote purposeful struggle and lead to purposeful products; work that students see as meaningful, relevant, and worth doing. Experience an example assignment and discover strategies that use reflection, feedback, and revision to make learning visible. Leave with practical ideas for fostering ethical AI use and designing assessments that balance product, process, and purpose. #purposeful-learning #productive-struggle #authentic-GenAI-assessment

Evidence-Based Strategies for Ethical AI Instruction in Higher Education If AI is reshaping every industry, why are so few students being taught how to use it ethically and effectively? This session presents research-informed strategies for teaching AI literacy while upholding academic integrity. Drawing from AI Unlocked, a stand-alone, first-year AI literacy course taught for multiple semesters at Saint Leo University, Amy Harris and Candyce Nelson will interactively share instructional models, student outcomes, and ethical frameworks. Collaborative activities include adapting rubrics and assignments across disciplines and exploring how to embed AI instruction into existing courses. Attendees will leave with practical resources and guidance for building or enhancing AI pedagogy without compromising rigor or ethics.#Freshman-AI-Literacy #Ethical Pedagogy #Academic Integrity

For those considering or already engaged in curriculum revision: Your students are already using AI. The question is, do you know how? Do you know how K12 students, your future students are using AI? In this session, we’ll pull back the curtain on the tools students are actually using today, including how these “study tools” are reshaping how work gets done. We’ll explore how these tools are changing student behavior, expectations, and skill development, and what that means for your classroom. We will also take a sneak peek at Turnitin's next-generation assignment types, designed to meet the needs of this era, where process has become much more important than product. If you are thinking about updating assignments or curriculum, this session will help you understand why now is the time. Walk away with practical insights to better support students in an AI driven world.

Generative AI is already reshaping how students learn to code, but “ban it” and “let it rip” both create problems. This session shares early evidence and practical course-design strategies from introductory programming contexts that vary in AI allowance and instructor guidance. We discuss how more open AI use can lift short-term assignment performance while increasing risks of overreliance, reduced conceptual understanding, weaker problem-solving retention, and academic integrity violations. In contrast, when instructors frame AI as a supportive but limited tool and embed process checks, students show more constructive attitudes and deeper learning signals. Attendees will leave with policy language, assignment structures, and guidance prompts ready to adapt. #GenAI #TeachingWithAI #AcademicIntegrity

When institutions ground AI policy development and governance decision in their mission and values, the results are energizing and transformative. This session equips you to guide your institution through a values-based approach to AI integration and governance. Through guided questions and a structured framework, you can engage faculty, staff, and leadership in a collaborative governance process that results in authentic, contextually appropriate AI integration. Examples will be shared showing how this approach helps develop policies that faculty and students embrace because they reflect the community’s core values. #AI-policy #shared-governance

Discussion about the state of AI at EBSCO and its impacts on areas of academic study.  
Topics include:

• Core tenants that drive AI development at EBSCO
• Real-world impacts of AI features on library engagement
• What’s next on EBSCO’s roadmap

This is an open session for presenters to ask any questions about the Teaching and Learning with AI conference repository. Presenters were previously notified about this new repository, and this session is an opportunity to learn more and ask questions about the submission process. The repository is an optional resource for presenters to share their presentation file(s) with a broader, public audience.

This interactive workshop empowers faculty, particularly those in service-oriented and applied disciplines, to design and integrate human-AI collaborative assignments that cultivate students’ prompting fluency, critical thinking, and reflective collaboration with AI. Through hands-on activities, discussions, live polls, and peer feedback, participants will explore scaffolded assignment frameworks that move learners from basic prompting to ethical evaluation of AI-generated outcomes. The session highlights practical design strategies, comparative tool insights, and approaches for responsibly preparing students for AI-driven workplaces of the future.#AIinEducation #GenerativeAI #FacultyDevelopment

Across campuses, the loudest conversation about AI centers on “AI-proofing” assignments and policing cheating. Great teaching does not ban tools—it designs for what the tool makes possible. This session shares a practical blueprint for AI-powered flipped learning in which first exposure happens through a standards-aligned AI tutor, while class time is reclaimed for coached problem solving and authentic application. Participants will explore design patterns that cut across disciplines—digital twins that simulate lab conditions before physical experiments, historical decision rooms that enable students to interrogate sources through agentic roleplay, writing copilots that scaffold revision and feedback, and career-connected projects that pair learners with task-specific copilots to build and critique portfolio artifacts.Rather than report research, this session delivers actionable takeaways: a modular framework for integrating AI tutors safely and effectively, a responsible-use playbook with bias-check and human-in-the-loop guidelines, and a model for small pilot programs that track cost per successful learning outcome. Attendees will leave with concrete tools to move from compliance to creativity—designing AI interactions that are safe, measurable, and discipline-authentic, transforming AI from a threat into a catalyst for learning.

In this session, we’ll demonstrate how AI can extend great teaching by supporting the full learning process, guiding students through explanation, practice, feedback, and reinforcement in alignment with instructor-defined goals.
Using Kyron Learning, you’ll see how educator-provided materials are transformed into structured, interactive learning experiences. Faculty define the learning objectives, select the content, and approve every lesson, ensuring that each AI-guided interaction reflects their intent and maintains academic rigor.
We’ll walk through a live demonstration of how students engage with AI-powered instruction: working through concepts step by step, applying what they’ve learned, receiving targeted feedback, and building confidence over time. The experience adapts to each learner while staying grounded in the course design set by the instructor.
You’ll also see how institutions are using this approach to deepen engagement, strengthen understanding, and improve measurable outcomes, while keeping educators firmly in control.
This is AI designed to support teaching and improve learning, not replace it.
What you’ll takeaway:

• How Kyron transforms course materials into guided, interactive lessons
• AI-driven experiences that support reasoning, practice, and feedback
• How faculty define learning objectives, control content, and approve every lesson
• Real examples of impact on student engagement, confidence, and outcomes
• A practical model for implementing AI in a way that aligns with how students learn

This workshop helps faculty, staff, and academic advisors understand and apply artificial intelligence in educational settings, with a focus on practical use and ethics. Participants explore current AI capabilities, hands-on strategies for creating content, redesigning academic processes, and enhancing student engagement while maintaining academic integrity. Through guided activities and discussion of ethical dilemmas, attendees gain tools to integrate AI into curricula to better prepare students for evolving workforce expectations.#AI--pedagogy, #Faculty-development, # Ethical-implementation