As artificial intelligence becomes more present in higher education, many of us are finding ourselves navigating new and sometimes uncertain territory, balancing questions about student use, academic integrity, and how this shift impacts us both professionally and personally. During this session we will focus on the human side of teaching and focus on how to set boundaries and reduce AI fatigue.
1.Reflect on how student AI use is impacting their teaching, expectations, and assessment practices
2. Identify key factors in determining when AI use is ethically appropriate
3. Develop strategies for responding to suspected or disclosed AI use in a constructive way
4. Recognize and process the personal and emotional impact of AI on their role as educators
Looking for the right AI partner in your teaching? With tools like ChatGPT, Gemini, Claude, and Magic School, it can be hard to know which one fits your needs.
This interactive session invites participants to explore AI through a creative, game-inspired experience centered on real teaching scenarios. Participants will compare how different tools respond to common classroom challenges—including questions like, “Can you write my paper?”—while reflecting on ethical and effective use.
Rather than identifying a single “best” tool, this session focuses on helping educators think critically about how AI aligns with their instructional values and goals. Through guided participation and discussion, attendees will consider how AI can support teaching practices without replacing the role of the educator.
The session will also highlight practical ways AI can enhance workflow, support differentiation, and improve accessibility.
Come curious—and ready to choose your match.
By attending this session, you will:
Explore the personalities (and strengths!) of popular AI tools
Rethink how AI can support—not replace—your teaching
Walk away with practical ideas for integrating AI into your teaching practice
What happens to student work when Canvas access ends? Notes, drafts, research, reflections — gone. That gap between what students build inside a course and what they carry with them afterward is a problem every discipline shares, even if it looks different depending on what you teach.
This session introduces Render, an AI-powered career dashboard built for Digital Media Arts students at Glendale Community College. Render is designed to bridge course competencies in AVC 248 (Design Self Promotion) with the career support students need after graduation. The tool guides students through seven structured areas: setting goals tied to a target job market, logging and analyzing job descriptions, drafting and revising resumes, identifying skills gaps, tracking networking contacts, practicing interview responses, and generating a personalized launch plan for the weeks after graduation.
What makes Render different from simply assigning AI use is structure and purpose. Each section uses AI to respond to what the student has already built: their goals, their saved jobs, their actual skills. No blank prompt required. When a student saves a job listing, an anonymized summary is shared with GCC Career Services, giving the college visibility into which employers students are pursuing and a basis for building internship relationships. Render is currently a working prototype, with a student pilot planned for Fall 2026.
Attendees will see the tool in action and leave thinking differently about how a structured interface can lower the barrier to AI adoption for students who find open-ended tools intimidating. No AI experience required.
1. Distinguish between assigning AI use and designing AI tools around student needs, and articulate why that distinction matters for student agency and engagement.
2. Identify at least one way a structured AI interface in their own discipline could reduce friction for students who find open-ended AI tools intimidating.
3. Describe how embedding AI into a purposeful workflow changes the student experience of using AI compared to prompting from scratch.
4. Recognize how interface design choices shape whether students engage with AI confidently or avoid it altogether.
The focus is to show some free tools/plaforms I have found that specifically help instructors. I anticipate the outcome is instructors will find practical uses for these tools, overcome hesitation in using them, learn to use them appropriatley and avoid the pitfalls of improper use.
The attendees will find practical uses for teacher-centric AI tools, learn to use them appropriately and avoid the pitfalls of improper use.
This proposal outlines an innovative, student-centered approach that integrates personality assessments, career exploration tools, and artificial intelligence to support identity development and career clarity. Students will first complete personality assessments to identify their strengths, interests, values, and workplace preferences. They will then complete career assessments that align these traits with potential professional pathways, ensuring a data-informed foundation for decision-making.
Using this information, students will engage with AI-powered image generation tools to create visual representations of themselves actively working in their selected careers. This intentional process allows students to translate abstract career goals into concrete, personalized images grounded in their assessment results.
Objectives: (1) Increase student self-awareness through personality and career assessments; (2) Strengthen alignment between individual traits and career pathways; (3) Utilize AI as a tool for career visualization and engagement; (4) Promote equity by expanding representation in professional spaces.
Learning Outcomes:
Students will be able to (1) analyze their personality and career assessment results to identify suitable career options; (2) apply assessment data to make informed career decisions; (3) create AI-generated images that accurately reflect their intended career paths; and (4) articulate how their identities, strengths, and interests connect to their future professions.
The project culminates in reflection and discussion, where students synthesize their assessment insights and AI-generated images, fostering confidence, intentional career planning, and a stronger sense of belonging in their chosen fields.
This interactive session explores how artificial intelligence (AI), storytelling, and career mapping can be integrated within First-Year Experience (FYE) courses and STEAM Collaborative initiatives to empower students to address real community challenges in South Phoenix. Grounded in culturally responsive pedagogy and community-centered learning, the session demonstrates how AI can be used ethically as a tool for inquiry, reflection, and solution design rather than simple content generation. Participants will examine a scalable instructional model that guides students through identifying local issues—such as heat inequity, digital access, education gaps, and health disparities—and connecting these challenges to their academic pathways and future careers.
Through structured activities, students engage in AI-assisted research, collaborative problem-solving, and digital storytelling to transform lived experiences into actionable community solutions. The model emphasizes student voice, representation, and the principle that those closest to community challenges should help design solutions. By combining FYE learning outcomes with STEAM-based innovation and Phi Theta Kappa’s leadership and service framework, the session highlights how interdisciplinary partnerships can increase student engagement, belonging, and purpose-driven learning.
Participants will leave with practical tools including sample prompts for ethical AI use, career-mapping strategies, and a replicable session framework adaptable across disciplines and institutions. Expected outcomes include increased student agency, improved critical thinking about AI and equity, stronger connections between education and workforce pathways, and enhanced capacity for students to see themselves as problem-solvers within their own communities. This approach positions AI not as a replacement for human insight, but as a catalyst for civic engagement, storytelling, and community impact.
By the end of this session, participants will be able to:
1. **Explain** how artificial intelligence can be integrated ethically into First-Year Experience (FYE) and STEAM learning environments to support student inquiry, reflection, and community engagement.
2. **Apply** a structured framework that connects storytelling, career mapping, and AI-assisted research to help students analyze and address real-world community challenges.
3. **Design** learning activities that guide students in linking their academic pathways and career goals to local social, economic, and environmental issues.
4. **Evaluate** strategies for using storytelling as a pedagogical tool to elevate student voice, lived experience, and culturally responsive learning practices.
5. **Implement** practical AI prompting techniques that promote critical thinking, collaboration, and responsible technology use rather than passive content generation.
6. **Adapt** a replicable interdisciplinary model that aligns FYE outcomes with STEAM initiatives, leadership development, and service-learning opportunities such as Honors in Action or community-based projects.
7. **Identify** methods for fostering student agency and belonging by positioning learners as community problem-solvers and future professionals contributing to regional impact.
Three English faculty are including CoPilot in a discussion about the future of First-Year Writing, exploring whether a Large Language Model can be made a useful collaborator in reshaping our disciplines for a world with AI.
Using LLMs as thought partners. Planning for an AI future.
This session demonstrates how NotebookLM can move beyond note-taking to function as a full learning system builder. Drawing from real course development in American Sign Language (ASL), I will show how I used NotebookLM to transform large volumes of instructional content into structured, scalable learning experiences.
Participants will see how AI can be used to generate lesson frameworks, scaffolded activities, assessments, and feedback systems aligned with pedagogical models (e.g., Activate–Support–Launch–Inspire). The session includes honest reflection on what worked (rapid content structuring, consistency across modules, idea expansion) and what did not (limitations in nuance, need for strong prompt design, and revision overhead).
Attendees will leave with a reusable “activity-in-a-box” workflow they can immediately apply to their own courses—whether for lesson planning, assessment design, or content transformation.
1. Design a structured lesson using NotebookLM by transforming existing course materials into an organized instructional sequence.
2. Apply a repeatable AI-assisted workflow to generate aligned learning objectives, activities, and assessments for their own courses.
3. Evaluate the strengths and limitations of AI-generated instructional content, including where human expertise is required for accuracy, nuance, and disciplinary integrity.
4. Construct effective prompts for educational use cases such as lesson planning, assignment creation, and feedback generation.
5. Implement an “activity-in-a-box” model that can be immediately adapted for use in their own teaching context or LMS.
6. Analyze how AI can support scalable course design while maintaining pedagogical quality and consistency across modules.
Providing timely, meaningful, and student-centered feedback is one of the most time-intensive aspects of teaching. This session introduces a practical, AI-supported workflow that helps faculty streamline grading while maintaining instructional intent, academic rigor, and authentic voice. Participants will explore how AI can transform rubric-based evaluations and informal instructor notes into clear, supportive, and actionable feedback aligned with assignment criteria.
Through a guided demonstration and hands-on practice, attendees will engage in a structured process that begins with assignment instructions and rubrics and culminates in refined feedback ready for students. The session emphasizes appropriate use of AI as a tool for refinement rather than replacement, with attention to accuracy, tone, and alignment with learning outcomes.
Participants will leave with a transferable workflow, a customizable prompt framework, and strategies for integrating AI into grading practices within Canvas LMS.
Apply an AI-supported workflow to transform rubric-based evaluations into clear, student-centered feedback.
Construct effective prompts that align AI-generated feedback with assignment criteria and instructional intent.
Evaluate AI-generated feedback for accuracy, tone, and alignment with learning outcomes and grading standards.
As generative AI becomes more common in education, faculty are often asked to both improve accessibility and explore AI integration in their teaching. This session connects those goals by focusing on how AI can be used to reduce barriers in course materials while supporting student learning.
Participants will explore how prompt design influences the quality and usefulness of AI generated outputs. Rather than treating AI as a content generator, this session frames it as a tool to support clearer instructions, more accessible materials, and more inclusive course design aligned with Universal Design for Learning principles.
Through a combination of brief demonstration and hands on activities, participants will practice using AI to improve instructional materials such as assignment directions and visual content. The session emphasizes critical use of AI, including when AI adds value, where it falls short, and the importance of instructor judgment in reviewing outputs.
Participants are encouraged to bring a sample assignment or material from their own course. Participants will leave with practical strategies and reusable prompt approaches that can be applied immediately in their teaching.
Apply a simple prompt framework to generate more effective AI outputs for instructional materials
Use AI to improve clarity and accessibility of course content
Evaluate AI generated content for accuracy, usefulness, and alignment with learning goals
This session introduces a human-centered approach to AI integration that begins with learning goals, student needs, and the principles of effective teaching. Rather than focusing on tools alone, participants will explore how AI can be aligned with course outcomes, support deeper learning, and enhance, rather than replace, human interaction in the learning process.
Participants will be introduced to a practical alignment framework that helps instructors evaluate AI use across key dimensions, including purpose, value, transparency, and impact on student agency. Through examples, the session will demonstrate how AI can be used to support instructional design, scaffold learning, and provide meaningful feedback while maintaining instructor presence and intentionality.
The session will also address common challenges, such as over-reliance on AI, unclear expectations for students, and concerns about academic integrity. Participants will reflect on how to communicate the role of AI in their courses and how to design learning experiences that encourage critical engagement with AI rather than passive use.
Interactive components will allow participants to evaluate sample AI use cases and consider how these approaches might apply to their own courses or institutional contexts. Emphasis will be placed on practical decision-making: when AI adds value, when it does not, and how to make those distinctions clear for both instructors and students.
By the end of the session, participants will have a clearer understanding of how to take a human-centered, aligned approach to AI integration; one that supports meaningful learning, ethical use, and student empowerment.
Participants will:
1. Apply a human-centered framework to evaluate AI use in teaching and learning.
2. Align AI-supported activities with course learning outcomes.
3. Identify when AI enhances or detracts from meaningful student engagement.
This interactive session shares the collaborative work of three education faculty teaching in teacher preparation programs at PC, EMCC, and MCC, developed during last summer’s Literacy Partners Project. Our work centers on supporting ethical, intentional, and discipline-relevant uses of artificial intelligence (AI) in higher education. As part of this ongoing effort, we have been developing a Canvas module that introduces students to widely recognized AI tools, with a focus on academic integrity, information literacy, and student agency. This module is a continuous work in progress, shaped by our teaching, collaboration, and reflection.
During the session, we will walk participants through key components of the module, including an instructional presentation on ethical vs. unethical AI use, a student-centered rubric with clearly defined expectations for acceptable AI integration, and a set of sample assignments. These assignments are designed to serve as a springboard—offering structure, language, and ideas that faculty can adapt and build upon within their own disciplines and content areas.
Our intent is not only to share the resources we have developed, but also to make visible our purpose for engaging in this work: to support students in using AI as a tool for thinking rather than a substitute for it. We approach this session as a space for dialogue, with the hope of learning alongside participants as we collectively explore how AI can be integrated in ways that uphold academic integrity, promote student agency, and remain responsive to diverse teaching contexts.
By sharing both our process and our evolving materials, this session invites participants into a collaborative exchange aimed at strengthening equitable and academically rigorous approaches to AI in teaching and learning.
Differentiate ethical and unethical uses of AI in academic work
Evaluate how AI integration supports information literacy and equitable teaching
Articulate an approach to integrating AI that supports student agency and integrity
Contribute to shared dialogue and learning surrounding AI integration
This interactive session explores how faculty can integrate artificial intelligence (AI) into inquiry-based learning in ways that strengthen student agency, information literacy, and equity-centered practice. Grounded in a classroom-tested unit from EDU230: Cultural Diversity in Education course, participants will experience a scaffolded sequence that begins with a KWL (Know–Want to Know–Learned) activity based on a contemporary podcast on the Pentagon’s use of AI. From this entry point, faculty will engage in the same process students follow: refining research questions, leveraging AI as a tool for idea development (not replacement of thinking), collaborating with library resources to locate credible sources, and reflecting through structured exit tickets and a student-centered rubric.
The session addresses a critical question in higher education: when and how does AI add value to student learning? Participants will examine strategies that promote transparency, academic integrity, and accessibility while avoiding over-reliance on AI-generated content. Through guided discussion and application activities, faculty will consider how to design assignments that require authentic thinking, connect to students’ personal interests, and build transferable research skills.
By the end of the session, participants will leave with practical tools—including adaptable assignments, reflection prompts, and assessment strategies—and a clearer framework for integrating AI in ways that enhance, rather than diminish, meaningful learning. This session is especially relevant for educators seeking to align AI use with equitable teaching practices and to support students in becoming critical, self-directed learners.
By the end of this session, participants will be able to:
Differentiate between productive and unproductive uses of AI in student research and inquiry tasks.
Design or revise one assignment component that intentionally integrates AI to support student thinking, rather than replace it.
Apply a scaffolded inquiry framework (e.g., KWL → research → reflection) to support student-driven learning and information literacy.
Evaluate how assignment design choices can promote or hinder student agency, academic integrity, and accessibility in an AI-supported environment.
Develop at least one strategy for partnering with library resources to strengthen students’ research skills and source evaluation practices.
Articulate how AI can be used to support equity-centered teaching practices within their discipline.
Faculty workloads continue to expand across teaching, assessment, communication, accreditation, and student support. While generative AI tools are widely discussed, many conversations focus on novelty rather than practical workflow integration. This session reframes AI not as a replacement for faculty expertise, but as a cognitive co-pilot that reduces repetitive workload while preserving academic judgment, mentorship, and instructional voice.
Participants will explore how generative AI can support four core areas of faculty work: course design, assessment and feedback, communication, and administrative documentation. Through live demonstrations and structured examples, the session will show how AI can assist with rubric-aligned feedback drafting, quiz and study guide generation, syllabus refinement, accessibility support (such as alt-text creation), and summarizing student feedback for instructional improvement.
The session will also address boundaries: when AI adds value, when it risks diminishing learning, and how to maintain transparency and academic integrity. Participants will leave with practical strategies for integrating AI into existing workflows, a framework for evaluating appropriate AI use, and sample prompts that can be adapted immediately.
By the end of the session, attendees will be able to identify one faculty task that can be responsibly augmented with AI, articulate guardrails for ethical use, and implement a sustainable approach that reduces burnout while enhancing instructional effectiveness.
By the end of this session, participants will be able to:
Identify at least three faculty workflow areas (e.g., assessment, communication, course design, administrative tasks) where generative AI can responsibly enhance productivity without diminishing academic rigor.
Apply a practical framework to evaluate when AI use adds value, including considerations of transparency, student agency, accessibility, and academic integrity.
Draft and adapt at least one AI-supported workflow (e.g., rubric-aligned feedback generation, quiz creation, syllabus refinement, or student feedback summarization) for immediate use in their own course or professional practice.
Articulate appropriate ethical guardrails and disclosure practices to ensure AI integration remains aligned with institutional policies and faculty professional judgment.
Designing Human–AI Workflows for Learning: A Practical, Tool-Agnostic Framework
Michelle Jung
As AI becomes embedded in learning design across education, training, and workplace development, practitioners face a common barrier: not a lack of tools, but a lack of structure. Many educators and learning professionals use AI reactively, resulting in inconsistent outputs, unclear processes, and unpredictable quality. This session offers a simple, tool-agnostic framework for structuring human–AI workflows that can be applied in any learning context, regardless of platform or model.
Participants will examine how learning tasks can be decomposed into components, assignable either to humans or to AI models, and how guardrails, constraints, and quality checks can improve reliability and reduce revision workload. Through short demonstrations and guided examples, attendees will see how small changes in workflow design can significantly improve clarity, consistency, and scalability across instructional materials, training modules, assessments, and content development.
This session is not a technical training and does not assume AI expertise. Instead, it introduces an adaptable mental model that participants can use with any AI system, including ChatGPT, Claude, Gemini, NotebookLM, and enterprise-level copilots. Attendees will leave with a one-page workflow template and a clearer understanding of how human judgment and AI support can be integrated to enhance learning design processes.
Most of the conversation around AI in higher education has centered on writing-intensive disciplines, and for good reason. But STEM faculty are navigating their own set of questions, often without a clear community to think alongside. This session starts from a simple observation: AI can solve most of the problems we assign in math courses, so what do we do about that? Rather than banning AI or ignoring it, I've spent the past year experimenting with assignment designs that make AI use purposeful in my calculus and college algebra courses. I'll share specific examples of tasks I've redesigned, where students are required to engage AI at a defined step, then evaluate, critique, or build on what it produces. The goal isn't to make assignments "AI-proof" but to make the thinking visible and AI use intentional.
I'll also share how I've been using AI in my own faculty workflow, from converting LaTeX documents into accessible formats, to aligning lesson notes with assignments and projects, and what that's taught me about where AI adds real value versus where it doesn't. But this session isn't just a show-and-tell. I want to open a conversation that I think is overdue: where is STEM in the AI conversation at community colleges? What are the unique tensions, and what can we learn from each other across disciplines? How do we teach students how to use AI effectively when we are still learning to do the same? Bring your questions, your skepticism, and your experiments!
Participants will be able to identify at least one strategy for redesigning assignments so that AI use is purposeful rather than just permitted or prohibited. Participants will examine the unique challenges STEM disciplines face with AI and consider how those challenges compare to their own discipline. Participants will leave with a practical framework for deciding when AI adds value to a task, whether for students or for their own workflow, and when it doesn't.
As generative AI becomes increasingly embedded in creative workflows, educators face a critical question: how do we integrate these tools without sacrificing creative thinking, originality, and student growth?
This session explores practical, real-world ways to incorporate AI into design education while maintaining a strong foundation in creative problem-solving and visual communication. Drawing from professional design experience and classroom instruction, this session demonstrates how AI can support ideation, iteration, and production without replacing the creative process.
Attendees will learn how to:
• Use AI tools like ChatGPT, Adobe Express, Firefly, Midjourney, Adobe Creative Cloud (and more) to accelerate ideation and content development
• Design assignments that encourage creativity, not shortcut it
• Teach students how to evaluate and refine AI-generated outputs critically
• Establish boundaries that support academic integrity and student ownership
AI is now woven into nearly every aspect of daily life. As an educator, I often feel challenged to keep pace with, if not stay ahead of, my students as they explore these rapidly evolving tools. Like most technological advances, generative AI brings both opportunities and challenges, and students are navigating its possibilities alongside us. However, they don’t always have the benefit of experience or a well-developed knowledge base to guide their decisions.
My goal is to encourage students to think critically about how they use AI and to consider its impact on their learning now and in their future careers. To support this, I’ve developed a digital literacy module to introduce various concepts about digital literacy and AI use. I have also asked students to include an AI statement with each assignment. The results—both in the assignments themselves and in the wide range of AI use—have been fascinating.
In our discussion, we’ll explore these outcomes and consider practical strategies for teaching students to use AI in ways that enhance their learning without diminishing the development of their own skills and understanding.
1. Participants will be able to implement an AI use statement or similar strategy to promote transparency and accountability in student assignments.
2. Participants will discuss practical approaches for integrating AI into coursework in ways that support student learning.
3. Participants will be able to guide students in developing critical thinking skills around AI use, including ethical and long-term academic and career considerations.
Faculty can apply the concepts from this session by adapting the approach to their own course content. Whether teaching writing, business, technical disciplines, or the humanities, instructors can maintain foundational skill development while using AI as a structured review partner that supports analysis, reflection, and improvement of student work.
Artificial Intelligence is rapidly entering higher education classrooms, but its most effective use occurs when it enhances - rather than replaces - student thinking. This session explores a structured approach to integrating AI into project management education while preserving the analytical foundations students must first develop.
In project management courses, students create a comprehensive set of professional artifacts across the project lifecycle. These artifacts are initially developed manually to ensure students understand the reasoning, structure, and discipline required to produce professional-quality deliverables.
Once these competencies are established, AI is introduced as an analytical assistant rather than a content generator. Students learn how to design prompts that guide AI to evaluate alignment between project artifacts, identify stakeholder gaps, analyze schedule dependencies, assess risk patterns, and improve communication clarity. AI tools can also review backlog prioritization, identify velocity trends in Agile environments, and synthesize project reporting for executive audiences.
The session will demonstrate how AI can support artifact validation, scenario analysis, and communication refinement while students remain responsible for decision-making and interpretation. Ethical considerations, including bias, transparency, data integrity, and responsible AI use are incorporated into the learning process.
Participants will leave with practical ideas for integrating AI into coursework in ways that strengthen analytical thinking, reinforce discipline in professional documentation, and model responsible AI use in applied learning environments.
This session is based on a recently submitted article describing the instructional approach used in Project Management courses that integrate AI as a structured analytical partner.
By the end of the session, participants will be able to:
1. Explain a structured framework for integrating AI into coursework without replacing foundational learning.
2. Design prompts that position AI as an analytical assistant rather than a content generator.
3. Identify ways AI can support evaluation and improvement of student-created artifacts and assignments.
4. Apply ethical considerations—including transparency, bias awareness, and validation—to AI-assisted learning activities.
As artificial intelligence becomes increasingly embedded in students’ academic work, faculty need practical ways to help students engage with these tools thoughtfully and responsibly. This session introduces “AI Literacy Lesson in a Box,” a ready-to-use classroom lesson that instructors can implement directly with their students and adapt to fit their specific discipline, assignments, and learning goals.
Participants will be guided through key elements of the lesson as both instructors and learners, experiencing activities designed to build students’ understanding of how AI tools work, where they succeed, and where they fall short. The lesson emphasizes critical engagement over avoidance, helping students evaluate AI-generated content for accuracy, bias, and usefulness while also exploring appropriate use cases such as brainstorming, drafting, and revision.
Help students critically evaluate AI outputs for accuracy, bias, and limitations
Guide students in identifying appropriate and ethical uses of AI tools
Integrate AI literacy into existing assignments without major course redesign