Teaching and Learning with AI Conference

Start your conference grounded, focused, and ready to engage! This seated yoga session is designed for a conference setting and can be practiced right at your table: no mat, no special clothing, and no prior yoga experience required. Led by Dr. Charlotte Jones-Roberts, RYT-200, Ed.D., yoga teacher and instructional designer, the session blends gentle movement, breath awareness, and mindful pauses to release tension, improve posture, and support mental clarity as you begin your day of sessions. Participants will leave feeling centered, refreshed, and more present for learning, connection, and collaboration throughout the conference.

As generative AI becomes part of everyday academic and professional work, higher education has an opportunity to move beyond policing AI use and focus on designing stronger, more meaningful assessments. In this session, USF Digital Learning designers share how they integrate AI as a thinking partner to support student learning while keeping students fully responsible for their work. Participants will explore practical design approaches, including prompt scaffolding and cross-verification assignments, that model how students can use AI responsibly and thoughtfully while strengthening critical thinking, creativity, and applied problem-solving. Attendees will leave with design strategies for maintaining academic rigor, improving assessment authenticity, and preparing students to navigate professional work in AI-influenced environments.#AuthenticAssessment, #StudentCenteredLearning, #LearningExperienceDesign, #AIThinkingPartner

As AI-detection tool usage increase expontentially, colleges risk defaming students when they treat detector outputs as proof of AI plagiarism. This session applies newsroom libel standards—falsity, publication, fault, and privilege—to academic misconduct processes that rely on AI detectors. Case law, university policy statements, and journalism guidelines, reveal how over-reliance on AI detection (including in appeals) can create negligence or a reckless disregard for truth (actual malice). The session also outlines assignment and syllabus designs that emphasize responsible AI engagement over policing.

Transitioning from face to face instruction to asynchronous learning raised concerns about losing the rich, real time reasoning practice students gain from interactive clinical cases. While students adapted well to online learning, recreating sequential, layered clinical reasoning without intensive instructor effort remained a challenge—until AI transformed the process. This session will demonstrate how AI can replicate the dynamic, conversational problem solving that occurs in live classrooms. By guiding learners through evolving clinical scenarios, AI enables real time reasoning, personalized feedback, and deeper engagement. Attendees will explore practical strategies for integrating AI to enhance clinical decision making in asynchronous environments.

Furman University's Pathways Program® is a mandatory two-year advising curriculum facilitated by faculty, staff, and undergraduate Peer Mentors with diverse expertise and experience levels. This session shares the development and Spring 2026 pilot of a purpose-built AI facilitation assistant designed to support advisors navigating a common curriculum across four semesters. By centering pedagogical flexibility within structured learning objectives, the assistant empowers educators to adapt activities to their cohort's unique needs while preserving non-negotiable program requirements — enabling instructors to honor both program integrity and the distinct personalities of their student groups. Attendees will hear honest pilot findings and are invited to consider how a similar model might support common course delivery at their own institutions. #autonomy #adaptation #integrity

This 10-minute presentation introduces a classroom assignment in which students design a multi-step creative AI prompt, run it across two different large language models, and iteratively revise the resulting outputs. By comparing how different systems respond to the same prompt, students learn to identify predictability, bias, and constraint in AI-generated writing. Guided iteration, remixing, and reflective self-assessment emphasize human agency, ethical judgment, and responsible use over efficiency or polish. The assignment offers a practical, adaptable framework for teaching critical engagement with generative AI through creative writing practice. (#writingpedagogy #responsibleAI #creativewriting)

This workshop introduces district leaders, school administrators, and K–12 STEM educators to hands-on computer science labs designed to build AI literacy and promote accessible technology practices. Participants will explore ADA-accessible labs from the accessible learning labs initiative, covering topics such as designing software for ADA compliance, as well as ethical considerations in AI, algorithmic bias, and cybersecurity. Through interactive modules, media, and quizzes, attendees will gain practical insights into accessibility and responsible AI development, empowering them to integrate these concepts into STEM curricula and create future-ready, K-12 students for the growing AI workforce sector.

Higher education is navigating evolving discussions about the responsible use of AI. Formal adoption often requires multi-level policy action. Academia must keep pace with the integration of AI, including ethical and responsible use. In this session, we’ll discuss AI policy development, AI-inclusive practices being explored and implemented, and teaching students ethical and responsible AI use. We’ll also provide participants with frameworks and comparative models for constructive campus dialogue. Participants will analyze scenarios, adapt language for their intended use, and identify steps to guide students in the ethical use of AI across campus. This session is appropriate for all conference participants.Keywords: ethics, policy, responsible use

Hungry for innovation in creating an engaging faculty development course about AI? Learn how we expanded our AI course offering to have both a beginner and advanced track. For our advanced track, we created a theme of an “AI restaurant” with a menu of options so that faculty could engage with a “choose-your-own-assessment.” Faculty had leeway in creating content relevant to their own classes that they could use immediately. We'll share what worked, what surprised us, and how you can adapt this model for your own institution.

AI guides for students are proliferating. However, many fail to meet legal accessibility requirements. This session showcases a WCAG‑compliant, student‑ready AI guide built by learning designers at a community college. We’ll unpack accessible design and discuss how Universal Design for Learning, raising metacognitive awareness, and other learning principles were integrated into the guide.  Attendees leave with an adaptable guide they can deploy tomorrow.  #Accessiblity #WCAG #Student-Guide

Assessment conversations around generative AI often default to concerns about academic integrity, leading to a binary distinction between “secure” and “open” assessment environments. In this framing, any unproctored or open context is assumed to involve AI use, limiting how faculty approach assessment design. Other frameworks present tiered-use approaches, but do not address design elements or task examples.
 
This session introduces a practice-informed framework for distinguishing AI-integrated and AI-resilient assessment approaches. Drawing on faculty development and instructional design work in higher education, the session will highlight key design characteristics that move beyond the secure/open binary and support more intentional alignment between learning goals and AI use.
 
Participants will explore:

• design characteristics of AI-integrated and AI-resilient assessments
• examples of assessment tasks across disciplines
• practical strategies for adapting existing assignments

 
The session is designed for faculty and instructional designers seeking concrete ways to rethink assessment in response to generative AI.