Current discussions of generative AI in creative and educational contexts often rely on tool-based or alien-intelligence metaphors, emphasizing control, and transactional prompting. This paper proposes an alternative framework—Human Relational Systems (HRS)—that reconceptualizes AI systems as human-derived intelligences operating across distinct representational and operational substrates, and argues that collaborative structure, rather than tool capability alone, shapes outcomes. Within the HRS framework, we distinguish three interacting forms of intelligence: Embodied Human Intelligence (EHI), representing human judgment, craft, and situated decision-making; Unembodied Human Intelligence (UHI), encompassing language-based, disembodied systems trained on human cultural artifacts; and Visual Human Intelligence (VHI), characterized by generative, spatial–temporal, and cinematic modes of representation. We propose that when these intelligences are deliberately integrated in a relational loop, they produce an amplified system-level outcome termed Joined Operational Yield (JOY), expressed as EHI + UHI + VHI = JOY. Drawing on practice-based examples from independent cinematic production workflows, we describe a triadic collaborative cycle where creative work moves iteratively across intelligences. In this cycle, EHI initiates intent through narrative and aesthetic goals; UHI supports ideation, scripting, and structural reasoning; and VHI generates visual sequences and cinematic interpretations. Outputs from VHI return to EHI for evaluation and refinement, completing the loop. Crucially, this process is not transactional or linear, but relational and iterative, relying on sustained context, role continuity, and shared goals rather than isolated prompts. The paper concludes by proposing HRS as a practical and ethical framework for hybrid human–AI collaboration, offering a transferable, practice-ready model for creative, educational, and research settings.

There are many versions that attempt to provide an accurate model for design thinking, but there is not a model that intuitively lays out the essential steps of the design process in its entirety. In addition, the ambiguity of design is often muddied with the incorporation of disparate concepts such as teamwork, the contrast of innovation, creativity and design, and the launch of the final product. These topics have high correlation to the design process, but are not intrinsically relevant to the process itself. The purpose of this design thinking project is to design a new model that addresses indigenous perspectives, the omissions of existing models, and a clear and intuitive framework that can be applied to a wide range of design projects.

As AI reshapes how students encounter the past, the central challenge is not whether tools can generate historical narratives, but whether learners can practice historical thinking with intention, creativity, and ethical responsibility. This interactive workshop uses Mercy Otis Warren, an influential Revolutionary-era writer with contested interpretations, as a case study for teaching women’s history through primary sources and historiography while responsibly integrating AI tools. Participants will work with a short primary-source excerpt set and a mini-historiography snapshot that frames competing interpretations of Warren: revolutionary feminist voice versus elite author reflecting privilege and prevailing gender norms. Using a structured, classroom-ready protocol, attendees will test how AI can support inquiry (question generation, rhetorical analysis prompts, counterargument building) while also identifying risks (bias amplification, invented citations, flattening of historical debate). Participants will leave with a reusable activity sequence, sample prompts, and a reflection framework that keeps human interpretation, and ethical teaching, at the center.

The rapid pace of technological advancement and industry disruption is redefining what it means to stay employable. As automation, AI, and shifting skill demands reshape the workforce, online educators face a pressing challenge: how to design learning experiences that not only teach current competencies but also cultivate long-term adaptability. This lightning talk explores how online higher education can serve as a strategic bridge between today’s learners and tomorrow’s careers.

Drawing on examples from microcredentialing, project-based learning, and industry-aligned course design, the session will highlight emerging practices that help learners “future-proof” their skill sets. It will emphasize practical strategies—like integrating digital literacy across disciplines, leveraging real-time labor market data, and fostering metacognitive skills—that empower students to navigate career change with confidence.

In the spirit of the lightning talk format, the five-minute presentation will briefly showcase an innovative framework for aligning online curriculum with workforce resilience. The following ten-minute discussion will invite participants to share insights from their own institutions, identify barriers to workforce-aligned education, and brainstorm collaborative solutions. By engaging with peers, attendees will co-create a richer understanding of how online education can move beyond credential delivery toward cultivating lifelong learners capable of thriving amid constant change.

This session will appeal to educators, instructional designers, and administrators seeking actionable ways to connect online learning with sustainable workforce development.

TCC 2026’s theme, Human by Design: AI, Creativity, and Purpose in Education, invites educators to look beyond tools and ask why and how AI should shape learning. This proposal shares how ʻIolani School is engaging students as co-leaders as we move from small AI pilots toward more coordinated efforts.

This session builds on last year’s TCC presentation, which introduced students as “AI-native learners.” This year, we focus not only on how students use AI, but on what happens when students help shape understanding, guide dialogue, and inform change. Through student-led AI presentations, tool testing, and participation in faculty professional learning, students have helped surface emerging needs and inform how our community approaches AI.

Students will share how these experiences have deepened their thinking about learning and responsibility. They will also reflect on what they believe educators most need to understand, including why guidance often matters more than restriction and why students want to be trusted as learners.

Student partnership has been especially important because many students are already navigating AI tools, norms, and peer conversations that rarely surface in classrooms. Incorporating student input has helped build shared understanding, identify gaps in guidance, and support educators who are still developing confidence engaging with AI. Even skeptical teachers often listen to students in a fresh and open way, creating space for dialogue that might not emerge through adult-led initiatives alone.

Participants will hear directly from students and examine student-teacher collaborative artifacts, focusing on concrete roles students can play in supporting AI-related learning and change. The session invites both experimentation and inquiry, offering insight into how student partnership can strengthen a school’s capacity to respond thoughtfully to emerging AI-related challenges.

Effective course design requires sustained creative energy. This energy can be hard to find in the middle or end of a semester or when assigned a new course to teach. While ChatGPT or other LLMs can help with brainstorming, a custom GPT can be trained on existing course documents. This training helps a custom GPT “remember” that initial context in each new conversation. This, in turn,  saves time and energy and can help maintain focus in course or assignment (re)design. In this presentation, I will briefly demonstrate the setup of a custom GPT and explore a use case for redesigning (parts of) a zombie-themed writing intensive course.

A mechanic explaining engine repair. A barista demonstrating latte art. A knitter teaching cable stitches. None have instructional design training. Most don’t identify as educators. Yet they teach millions daily through TikTok, Instagram Reels, and YouTube Shorts.

Researchers call this “nano learning”: focused learning objectives delivered in brief, highly engaging segments. These creators intuitively apply strategies that align with cognitive load theory, Mayer’s multimedia principles, and social learning theory. They do this without ever studying them.

This session demonstrates “The People’s Professors,” an interactive game-based learning experience that helps instructional designers recognize and apply nano learning strategies from accidental educators. Participants will explore the “Kingdom of Engagement,” collect five Pedagogical Artifacts (Hook Gem, Chunk Crystal, Visual Scroll, Participation Orb, Series Compass), and interact with the “Spot the Strategy” game.

The project addresses “Human by Design” on two levels. First, it studies the most human teachers: ordinary people who connect with audiences through intuition, not algorithms. Second, it models AI as a developer sidekick, not a replacement. Using “vibe coding” (AI-assisted development), I built this tool by describing what I wanted and iterating with AI. This approach makes game-based learning development accessible to designers without traditional programming skills. The AI helped me build. The human insights made it meaningful.

Participants leave with practical frameworks for translating creator techniques into formal learning contexts and a model for using AI as a creative partner.

As generative AI becomes increasingly embedded in education, creativity is often mistaken for effective prompting, and reflection is reduced to a standardized activity that can be automated rather than a genuine intellectual exercise. When students rely on generative AI for immediate responses, they bypass opportunities for creative thinking and metacognitive reflection, undermining the cognitive processes that reflection is designed to support. Grounded in metacognitive theory, reflection is understood as a deliberately cultivated process in which learners monitor, evaluate, and make meaning of their own thinking (Flavell, 1979; Schön, 1983).

While AI can generate content, it cannot generate purpose, judgment, or self-awareness. These are instructional outcomes that instructors must intentionally design. Melendez noted, “human experience and judgment are still critical to making decisions, because AI cannot reliably distinguish good ideas from mediocre ones or guide long-term strategy on its own” (2025). Instructors should carefully design educational activities that encourage students to think about their own thinking rather than consume AI-generated outputs. This interactive session will engage participants in examining how creativity and reflection can be intentionally cultivated in AI-supported learning environments.

Participants will work with sample assignment prompts and discuss how design choices can either invite or inhibit creative and reflective thinking. Through live polling, short reflective prompts, and collaborative whiteboard activities, participants will explore the difference between AI-generated output and human originality. By the end of the session, participants will have foundational knowledge of how to apply metacognitive strategies to foster originality, creativity, and reflection in AI-supported learning environments.