CSU AI Educational Innovations Challenge (AIEIC)

CSU AI Educational Innovations Challenge (AIEIC) is sponsored by California State University. Encourages faculty to integrate AI tools into curriculum and instruction to enhance critical thinking and promote ethical AI use.

CSU Artificial Intelligence Educational Innovations Challenge Award Showcases | CSU AI Commons CSU Artificial Intelligence Educational Innovations Challenge Award Showcases The California State University ( CSU ) system launched the Artificial Intelligence Educational Innovations Challenge (AIEIC), inviting CSU faculty to develop innovative instructional strategies that leverage artificial intelligence (AI) to enhance critical thinking, promote the ethical and responsible use of AI, and integrate AI literacy across curricula.

Below, you will find a collection of faculty showcases highlighting the remarkable creativity and innovation taking place across disciplines. These projects demonstrate a wide range of approaches to thoughtfully integrating AI into courses and academic programs, offering new models for teaching, learning, and student engagement in an AI-enabled educational landscape.

Utilizing the Information Literacy Framework for the Computer Networks Course at CSUB Throughout the project, faculty perspectives evolved from asking whether AI should be allowed in the classroom to exploring how intentional assignment design can foster deeper analysis, evaluation, creativity, and reflection.

Rather than treating AI as an obstacle to learning, participants discovered ways to use it as a catalyst for richer critical thinking while maintaining clear expectations for ethical and transparent use. The GAME Plan demonstrated that meaningful faculty development extends beyond introducing new technologies.

By emphasizing pedagogy, collaboration, and practical course redesign, the project equipped instructors with immediately usable resources and renewed confidence in preparing students for an AI-rich world. Generative AI is rapidly changing how students approach coursework, creating both opportunities for learning and concerns about overreliance on AI-generated answers.

This project explored an alternative to prohibiting AI use by intentionally integrating AI into an undergraduate Computer Networks course at California State University, Bakersfield. The goal was to transform AI from a general-purpose answer generator into an assignment-specific learning assistant that supports critical thinking, problem solving, and responsible AI use.

Using OpenAI’s custom GPT tool, a collection of AI tutors was developed for specific course activities. Each tutor was provided with course- and assignment-specific knowledge and designed with clear instructional boundaries.

Rather than generating completed code or directly providing solutions, the tutors were instructed to guide students through questions, debugging, conceptual explanations, pseudocode, and problem-solving strategies. Student interactions and feedback were reviewed throughout implementation and used to refine subsequent tutors.

The project involved 70 students across two semesters and was evaluated through surveys administered at the beginning, middle, and end of the semester. Student perceptions improved across all eleven measures examined, with particularly strong gains in inclusive learning and accessibility. A key lesson is that simply giving students access to AI does not make AI educational; its value depends on intentional instructional design.

Assignment-specific AI tutors, combined with clear boundaries and faculty oversight, can provide on-demand support while preserving independent thinking. This project offers a scalable model for helping students use AI critically, responsibly, and productively.

Outcomes & Resources for Utilizing the Information Literacy Framework for the Computer Networks Course at CSUB The GAME Plan: Designing Critical Thinking Assignments for the AI Era Critical Thinking for all Disciplines The GAME Plan: Designing Critical Thinking Assignments for the AI Era challenged faculty to rethink teaching as a creative, strategic adventure in an educational landscape increasingly shaped by generative AI.

Built around the GAME framework (Generate, Analyze, Modify, and Elevate), the project guided faculty through a hands-on process of examining AI's capabilities and limitations, redesigning assignments, and creating clear, student-centered approaches to AI use in their courses.

Originally piloted as an in-person workshop and later expanded into a fully asynchronous, gamified Canvas experience, The GAME Plan engaged faculty from across the university in collaborative learning focused on strengthening critical thinking rather than simply limiting AI use.

Participants redesigned existing assignments into “Critical Thinking Quests,” developed customized AI use policies using the “AI Use Policy Builder,” and explored practical strategies for helping students analyze, critique, and thoughtfully engage with AI-generated content.

Throughout the project, faculty perspectives evolved from asking whether AI should be allowed in the classroom to exploring how intentional assignment design can foster deeper analysis, evaluation, creativity, and reflection. Rather than treating AI as an obstacle to learning, participants discovered ways to use it as a catalyst for richer critical thinking while maintaining clear expectations for ethical and transparent use.

The GAME Plan demonstrated that meaningful faculty development extends beyond introducing new technologies. By emphasizing pedagogy, collaboration, and practical course redesign, the project equipped instructors with immediately usable resources and renewed confidence in preparing students for an AI-rich world.

Outcomes & Resources for The GAME Plan: Designing Critical Thinking Assignments for the AI Era Redesigning Critical Thinking in Communication: Integrating AI as Text, Tool, and Topic.

AI Literacy in Communications Although many academic libraries have created online guides to define and distinguish information produced by GenAI tools versus human authors, more hands-on learning activities may improve student understanding of how artificial intelligence can shape information use, creation, or dissemination.

We propose redesigning an information literacy course with extensive in-class practice with AI tools exploring critical and ethical applications. Moreover, accompanying modules will be made available to all university students regardless of their enrollment status. This allows other faculty to import any module to their coursework.

By developing an information literacy course curriculum that includes AI literacy education and offers open-access modules, we hope to deepen students’ information and AI literacy skills as well as offer flexible and potentially scalable AI literacy instructional design that can be integrated into CSU learning environments.

Outcomes & Resources for Redesigning Critical Thinking in Communication: Integrating AI as Text, Tool, and Topic.

Paper Trails: Developing a Scaffolded, AI-Assisted, Detective-Narrative Module for Teaching Scholarly Writing in the Age of AI Technical Writing in STEM Courses This project flipped the script of academic research and writing by drawing upon the familiar narrative arc of a detective series to make the writing process visible.

Inspired by the methodical approach of a beloved TV detective, the famously sharp and underestimated Lieutenant Columbo, our project guides students to follow leads, question assumptions, and ask “just one more thing” as they engage in academic research and writing.

We revised and expanded our multi-phased AI-assisted framework using distinct tools and customized agents, informed by how our students actually used the tools in AY 2025-2026. Our adaptable framework now better supports faculty across disciplines and helps students leverage AI to spark robust inquiry and interrogation.

This project will refreshed our scalable, cross-disciplinary resources to support ethical, inquiry-driven scholarly writing in the age of AI.

Outcomes & Resources for Paper Trails: Developing a Scaffolded, AI-Assisted, Detective-Narrative Module for Teaching Scholarly Writing in the Age of AI Knowing Your AI Through Fun and Critical Engagement Multiple Disciplines, AI Literacy Knowing Your AI Through Fun and Critical Engagement was a Canvas-based AI literacy project that introduced approximately 70 students to practical, critical, and ethical uses of generative AI.

Through an approximately three-hour activity, students used CSU -supported ChatGPT and other AI tools to explore how AI can support academic work while also learning to evaluate its limitations and risks. Pre- and post-exercise surveys indicated changes in students’ attitudes toward and knowledge of AI use.

The project demonstrated that students can learn to use AI more effectively when they are given structured opportunities to engage with AI, test its reliability, and reflect on ethical implications. The project also produced reusable Canvas materials and survey instruments and was showcased at the AI Convening in San Diego.

Outcomes & Resources for Knowing Your AI Through Fun and Critical Engagement Expanding and Improving the AI Retrofit at Chico State Faculty Dev, Multiple Disciplines The AI Retrofit has been assessed and is a viable model for adapting to AI. This systematic approach identifies AI disruptions, helps faculty locate solutions, and has concrete deliverables.

The program develops pedagogical innovation at the course level which is then shared with future participants. The programming is built on ethical engagement with AI incorporating student perspectives, conversations about bias, sustainability, and privacy. This funding would allow the program to continue despite a budget contraction.

We have also developed an asynchronous version of the retrofit. The model is the same with a focus on learning outcomes before assignments. The other portion of funding in this application is to incentivize the completion of the work.

This aspect scales the project in a different way that could extend well beyond the Chico State campus.

Outcomes & Resources for Expanding and Improving the AI Retrofit at Chico State Revamp Your Course with AI Faculty Dev, Multiple Disciplines Facilitated by the Faculty Development Center at CSU Dominguez Hills, the Revamp Your Course with AI (RYCWAI) project engaged with faculty members across two intensive workshops, focused on the thoughtful integration of generative AI into teaching and learning.

Through two four-session workshop cohorts offered during Fall 2025 and Spring 2026, faculty examined the opportunities and challenges of generative AI while exploring issues of academic integrity, ethics, equity, bias, among others. Participants redesigned student learning outcomes, assignments and assessment strategies to promote critical thinking, responsible AI use and authentic student learning.

The project fostered interdisciplinary collaboration and provided faculty with practical strategies for incorporating AI parting from their teaching styles, pedagogies, interests and concerns, while emphasizing transparent conversations with students about AI's capabilities and limitations.

Evaluation findings demonstrated increased faculty confidence in integrating AI into course design, widespread implementation of redesigned assignments across disciplines and early evidence of improved student engagement through more reflective, authentic and process-oriented learning activities.

The workshop materials, instructional resources and redesigned assignments developed through RYCWAI provide a scalable model for supporting AI literacy and responsible AI integration across the CSU system. Additional Reflections/Recommendations Future AI initiatives should consider different faculty development trainings.

Faculty beginning to explore generative AI need foundational information about how the tools work, academic integrity, equity and ethical concerns. Faculty with more experience need advanced opportunities focused on discipline-specific applications, assignment redesign and tech comparisons. While a single workshop can meet these interests, it can be more productive if separated.

Outcomes & Resources for Revamp Your Course with AI CLAIMS: Collaborative Learning with Artificial Intelligence in the Mathematical Sciences CLAIMS (Collaborative Learning with Artificial Intelligence in the Mathematical Sciences) launched a Faculty Learning Community (FLC) to help mathematics faculty integrate generative AI into undergraduate instruction in ways that support learning, metacognition, critical thinking, and ethical engagement.

Rather than approaching AI primarily as a tool for efficiency or answer generation, the project explored how generative AI could serve as a thinking partner, helping students ask better questions, test ideas, identify errors, revise explanations, and reflect on their own learning.

At the same time, the project sought to build faculty capacity to thoughtfully evaluate emerging AI technologies and design learning experiences that leverage AI to support, rather than replace, student thinking. The project largely unfolded as proposed while naturally evolving alongside rapid developments in generative AI.

Eleven mathematics faculty members (both tenure-track and lecturers), including three facilitators, participated in the year-long learning community, and nine faculty completed classroom inquiry projects implemented across courses including College Algebra, Calculus, Calculus III, Statistics, and mathematics courses for future teachers.

The summer kickoff introduced participants to generative AI, Reading Apprenticeship, metacognition, prompt engineering, the Cycle of Inquiry, and ethical AI use. Throughout the academic year, monthly meetings combined hands-on exploration of AI tools with discussions of pedagogy, scholarship, and classroom practice.

Faculty explored topics such as Custom GPTs, accessibility, student attitudes toward AI, institutional AI policies, and strategies for evaluating student thinking. Guest speakers shared experiences integrating AI into mathematics, digital humanities, and computer science courses, broadening participants' perspectives and providing concrete examples of effective classroom implementation.

One of the project's greatest accomplishments was fostering faculty AI literacy alongside student learning. As the year progressed, participants developed a deeper understanding of how large language models work, where they are effective, where they fail, and how issues of ethics, accessibility, academic integrity, and institutional policy intersect with classroom practice.

Rather than relying on one-time training, the sustained Faculty Learning Community created space for faculty to experiment, share successes and challenges, compare student experiences, and refine instructional approaches together. Participants consistently reported that seeing authentic classroom examples from colleagues made AI integration feel practical, adaptable, and grounded in the realities of teaching mathematics.

CLAIMS also produced a collection of classroom-tested instructional strategies that extend beyond the original project. Facilitators developed learning activities introducing how generative AI works, prompt engineering guides, Custom GPT workshops, ethics and accessibility activities, and inquiry-based faculty learning materials.

Several of these resources have already been adapted for professional development with K–12 educators, workshops for university students, and faculty beyond the original learning community.

The overall model, combining Reading Apprenticeship principles, collaborative faculty inquiry, and classroom experimentation, provides a framework that can be adapted for future professional learning communities exploring AI or other emerging technologies. Faculty inquiry projects demonstrated a wide range of approaches for integrating AI into mathematics instruction.

Participants designed activities that positioned ChatGPT as an "Error Detective," "Practice Partner," "Translator," or "Alternate Solver," encouraging students to critique AI-generated work, compare multiple solution methods, generate practice problems, explain mathematical reasoning, and reflect on AI's strengths and limitations.

Faculty collected evidence through student surveys, reflections, assignments, AI interaction records, homework data, assessment results, and classroom observations, culminating in presentations that shared instructional artifacts, findings, and recommendations for future practice. Several important lessons emerged from the project. First, effective AI integration depends far more on instructional design than on the technology itself.

Students benefit most when AI use is intentionally structured and accompanied by opportunities to explain their reasoning, evaluate AI-generated responses, revise mistakes, and reflect on their own learning. Second, both students and faculty continue to develop AI literacy.

Faculty found that many students did not initially recognize the variety of ways they were already using AI, underscoring the importance of explicitly teaching what AI use looks like, how AI systems generate responses, and why critical evaluation remains essential. Finally, the project reinforced that meaningful AI integration requires sustained faculty learning.

Building confidence with AI is not simply about learning new tools; it involves developing shared understandings of pedagogy, ethics, accessibility, assessment, and disciplinary practice through ongoing collaboration and reflection. The project also highlighted several challenges. Designing AI-supported learning activities that promote reflection rather than answer generation requires significant time and iteration.

Student engagement with AI varied widely, and AI itself did not address broader challenges related to motivation, persistence, or course completion. Faculty also found it difficult to isolate the effects of AI from other instructional changes occurring simultaneously.

These experiences reinforced that AI should be viewed as one component of a broader instructional ecosystem that includes clear expectations, feedback, opportunities for revision, and supportive learning communities. What is one thing you would most like the CSU community to learn from your project?

The most important lesson from CLAIMS is that AI integration is not mainly about teaching students to use a tool; it is about designing learning experiences that help students think more critically, reflect more intentionally, and engage more ethically with emerging technologies. AI can support learning when students are asked to question it, test it, revise with it, and explain their own thinking in relation to it.

However, these habits do not develop automatically. They require thoughtfully designed assignments, clear expectations, and opportunities for metacognitive reflection. Our experience also showed that this kind of instructional design depends on sustained faculty learning and collaboration.

When faculty have the time and support to explore AI together, they are better equipped to create learning experiences that help students use AI critically, ethically, and productively.

Outcomes & Resources for CLAIMS: Collaborative Learning with Artificial Intelligence in the Mathematical Sciences AI-LEARN: Artificial Intelligence for Literacy, Ethics, Application, and Reasoning in Next-gen learners This project redesigns three foundational computer science courses—CSC115, CSC121, and CSC301—to integrate AI tools, literacy, and ethics while addressing the emerging AI divide.

In CSC115 (Introduction to Programming Concepts), students will use AI tools (e.g., ChatGPT, GitHub Copilot, Gemini) for problem-solving, critical thinking, and understanding programming constructs. In CSC121 (Introduction to Computer Science & Programming I), students will learn prompt engineering techniques to solve programming problems and deepen algorithmic understanding.

In CSC301 (Computers & Society), students will engage in case studies using Research Libraries Guiding Principles for Artificial Intelligence, and ACM’s Principles for the development, deployment, and use of Generative AI Technologies, reflecting on bias, misinformation, and human accountability.

This proposal promotes AI literacy, enhances critical thinking, and introduces innovative instructional strategies that leverage AI tools to produce scalable, modular content for CSU system-wide adoption.

Outcomes & Resources for AI-LEARN: Artificial Intelligence for Literacy, Ethics, Application, and Reasoning in Next-gen learners Introducing AI Literacy in General Education History Courses for College Success and Civic Participation As generative AI becomes a permanent feature of higher education, faculty need evidence-based approaches that integrate AI without diminishing disciplinary learning.

This project designed, implemented, and evaluated AI-infused assignments across five General Education history courses enrolling approximately 225 students at California State University, East Bay. Rather than encouraging reliance on AI, each assignment required students to corroborate AI outputs through historical evidence and disciplinary methods.

IRB-approved pre- and post-assignment surveys found that students improved their AI literacy, confidence in historical thinking, and awareness of ethical issues surrounding AI while remaining appropriately skeptical of ChatGPT's reliability. Students also reported greater commitment to preserving their own independent thinking when using AI.

These findings suggest that carefully scaffolded, discipline-specific assignments can help institutions prepare students to engage generative AI thoughtfully, critically, and responsibly without sacrificing the core learning outcomes of General Education. As attachments, we have included the following artifacts and assets that may be shared with the CSU community and public.

Click on the Showcase link to view the following resources: AI-integrated Assignments (6) Five AI-infused assignments designed by History faculty and deployed in Spring 2026 General Education courses, including both lower- and upper-division classes.

Three separate conference presentations of our work in 2026, including at the American Association of Colleges and Universities Annual Meeting and the Western Psychological Association Annual Convention. We also shared our results within the California State University system, presenting our work at a CSU -wide virtual event.

IRB-approved evaluation instruments (2) We designed and deployed these two IRB-approved survey instruments to measure the effectiveness and impact of our AI assignments. Video walkthrough of main findings (1) This describes our main statistical findings from student surveys evaluating the effectiveness and impact of our AI-infused assignments. A CSU ZOOM webinar presentation recording.

Outcomes & Resources for Introducing AI Literacy in General Education History Courses for College Success and Civic Participation Embedding AI Literacy and Use Across CSUEB’s Writing Programs GE 1A, Writing Intensive Classes Cal State East Bay’s writing programs participated in AIEIC by piloting AI literacy activities and assignments across the sequence of writing-intensive classes offered on our campus, from GE Areas 1A (written composition) and 1B (critical thinking and composition) and the University Writing Requirement (East Bay’s GWAR/Graduation Writing Assessment Requirement) to upper-division GE and major courses with significant writing requirements.

Outcomes & Resources for Embedding AI Literacy and Use Across CSUEB’s Writing Programs AI-REFINE (Artificial Intelligence for Responsible, Ethical and Faculty-Informed Next-Gen Education) was designed to prepare Fresno State faculty to integrate generative AI into teaching in ways that enhance student learning and critical thinking while promoting responsible and ethical AI use.

Building on a pilot Institute and informed by its evaluation findings, the three-day, in-person AI-REFINE Institute in August 2025 engaged approximately 30 faculty in hands-on exploration of AI and redesign of assignments and other course components for Fall 2025 implementation. Participating faculty subsequently implemented redesigned practices across 63 courses enrolling more than 3,000 students.

Mixed-methods evaluation showed substantial growth in faculty preparedness to teach with AI, including greater knowledge, confidence, and capacity to integrate AI into instruction. Faculty also became more critical and intentional in their approaches to AI, including greater attention to the uncertainty and verification of AI-generated information.

Follow-up evidence documented clearer AI expectations, assignment redesign emphasizing evidence and processes of learning, and structured uses of AI to support learning. Student perspectives identified benefits for explanation, practice, and efficiency, alongside uneven instruction, inconsistent expectations, concerns about accuracy and overreliance, and a need for greater guidance in effective and ethical AI use.

Findings from faculty and students show that responsible AI integration in higher education depends not simply on adopting AI tools, but on intentional pedagogy, critical AI literacy, clear expectations, and continued opportunities to learn from implementation.

Development of Pedagogical Toolkits for AI-Enhanced Financial Decision Making This project transformed FIN 121-Intermediate Financial Management course, a required course for undergraduate business majors, by incorporating state-of-the-art Generative AI (GenAI) tools to enhance students’ critical thinking skills and equip them with the competencies required for AI-driven workplaces.

The initiative demonstrated GenAI applications in financial analysis, deepen comprehension through guided and interactive prompt engineering, and develop quantitative reasoning and deductive skills. To achieve these objectives, we designed innovative lessons and assignments where students collaborate with AI to conduct financial analysis and learn how to evaluate AI-generated outcomes.

Our formative and summative assessments measured students’ ability to distinguish appropriate AI assistance from over-reliance on AI and potential AI hallucinations. The revamped course included new modules on Fintech, Blockchains, and ethical frameworks for responsible AI use in financial decision-making. The team developed zero-cost course materials to share across CSU communities via CSU AI Commons.

Outcomes & Resources for Development of Pedagogical Toolkits for AI-Enhanced Financial Decision Making MCJ Innovation Lab: Ethical AI Integration in Storytelling and Critical Thinking in Media Communication and Journalism Education Media, Communications, & Journalism GE & Upper Div Storytelling The MCJ Innovation Lab is a faculty-led initiative within the Media, Communications and Journalism (MCJ) Department at Fresno State, aiming to develop scalable, equity-focused curricular models that integrate emerging technologies, such as AI, data visualization, and immersive storytelling, into undergraduate media journalism education.

Grounded in the department’s newly developed Learning Goal 6, the project engaged students in critically analyzing AI-assisted and generated content in media production and reflecting on ethical implications in journalism, advertising, and multimedia storytelling. Faculty co-developed assignments, rubrics, and workshops that promote learning with and through AI—never in place of critical thinking or creativity.

The project spans GE and upper-division courses and produced an open-access toolkit and digital showcase for CSU AI Commons, offering a replicable model for AI literacy and ethics across the CSU system. What is one thing you would most like the CSU community to learn from your project?

Journalism education must learn to coexist with AI, which will become an increasingly influential part of the media industry, rather than treating it solely as an adversary. AI literacy is not simply the ability to generate content more quickly; it is the ability to think critically, question assumptions, verify information, and recognize what should never be delegated to a machine.

Effective AI education in journalism and media studies must preserve human judgment, ethical responsibility, original reporting, creativity, and lived experience.

Outcomes & Resources for MCJ Innovation Lab: Ethical AI Integration in Storytelling and Critical Thinking in Media Communication and Journalism Education LIFT: Learning with AI, Implementing Change, Fostering Equity, Transforming Teaching This proposal reimagines AI integration in STEM education through a student-centered and scalable approach.

Across high-impact, equity-challenged courses, student collaborators will co-design AI-infused assignments tailored to real classroom challenges they face. Each activity will be tested through a randomized controlled trial, comparing AI and non-AI strategies matched to identical learning objectives.

Instructors and students will partner to develop these interventions and analyze their effectiveness through a rigorous, data-driven process. Classroom implementation will foster peer dialogue around AI's capabilities, limitations, and ethical use—broadening AI literacy across diverse student populations.

Deliverables include a public repository of AI-infused assignments, a CSU AI Commons toolkit with co-creation protocols and ethical use guidelines, and a cross-course implementation playbook for faculty replication. By embedding AI into inclusive pedagogy and critical inquiry, this project empowers ethical, equitable, and transformative teaching and learning across the CSU system.

Past, Present, and Future: Intentional and Ethical Activation of AI in the Teaching of U.S. History Survey Courses The accelerating presence of artificial intelligence (AI) in daily life demands that instructors and students not only understand the technical capacities of AI but also develop critical frameworks for evaluating its influence on practices of thinking, research, writing, and historical narratives.

Through a series of collaborative and interactive workshops for History faculty, we propose to redesign the lower-division general education U.S. history survey courses at CSUF (HIST 170A, HIST 170B, HIST 180, HIST 190) to ethically integrate AI literacy and critical thinking skills through learning about AI tools in Fall 2025, and then designing and testing innovative instructional models, assignments, and assessments in Spring 2026.

Over 20 faculty collectively teach on average 40 sections of the U.S. history survey each semester. They will be invited to participate, which could potentially impact up to1,600 students every semester.

Why and How to Teach History in the Age of AI: Recovering Our Classrooms and Centering Human Learning Why and How to Teach History in the Age of AI: Recovering Our Classrooms and Centering Human Learning Final Workshop Presentations This link will take you to an external website in a new tab.

AI Capabilities vs. Faculty Experiences vs. Student Use Faculty Fears, Hopes, Questions Nick Enke: AI Capabilities and Student Uses Source Analysis and Student Voices in Writing Process-centered Teaching & Alternative Forms of Storytelling Workshop and Designing a Department Statement on AI To summarize, these workshops showcased that teaching history well in an Age of AI requires revisiting our approaches.

The intentional incorporation of guided LLM-chatbot uses will enhance students’ critical AI literacy and technological competencies. In addition and more conceptually, we discovered the value of instruction that values process over product and inquiry, creativity, and critical engagement over stolid knowledge.

Assignments that emphasize contextual reasoning, ethical judgement, personal voice and interpretive analysis reduce opportunities for AI misuse or abuse while promoting deeper and more transformative learning of history.

Outcomes & Resources for Past, Present, and Future: Intentional and Ethical Activation of AI in the Teaching of U.S. History Survey Courses Cultivating a New Culture of Thinking, Learning and Being in the AI Driven 21st Century GE Reading, Thinking & Literacy This project addressed the “GenAI and Expertise Paradox,” (Mishra, 2025) where reliance on generative AI tools can lead to fluent but shallow responses, hindering critical thinking and metacognitive skills.

In response, we proposed redesigning READ 290: Critical Reading, Thinking, and Literacy, a GE course that enhances students’ critical thinking skills with academic texts.

Taught in English and Spanish, using bichronous online and in-person formats, and serving a diverse student population, including first-year and EOP students, the redesign used Design Thinking principles, to embed ethical, reflective, and intentional AI use, fostering a new culture of thinking, learning and being.

With 40+ sections and 1,300 students annually, READ 290 offers a scalable platform for AI-driven innovation in critical thinking. Teaching this redesigned course was, without exaggeration, one of the most rewarding experiences of my career, and student engagement with the material bore that out. This project also sharpened a question I expect to keep working on well beyond this grant.

Generative AI is remarkably good at producing fluent, organized language, but fluency is not the same as understanding. What a system generates is a surface form; the reasoning, judgment, and lived experience that give that surface form meaning are not things AI possesses on its own, they belong to the person doing the thinking.

The core work of this course, and the work I see ahead of me, is helping students learn to tell the difference: to recognize when language sounds like thought but is not yet their own, and to insist on doing the thinking that gives their words weight, even when a faster, fluent alternative is one prompt away. Thank you for the opportunity to pursue this work.

Redesigning READ 290 confirmed for me that the questions this project raises, about knowledge, reasoning, making meaning, and what AI can and cannot do for a thinking mind, are only going to matter more, not less, and I am grateful to have had the support to begin answering them in a real classroom rather than in the abstract.

Outcomes & Resources for Cultivating a New Culture of Thinking, Learning and Being in the AI Driven 21st Century ATLAS: Applied Thinking & Learning through AI Storytelling.

Hart, Chang, and Sotomayor Applied Storytelling, Critical Thinking (Area 1B) This project, and the class born from it, provides students with the opportunity to develop and strengthen two critical professional skills: the ability to communicate comfortably and effectively across institutional hierarchies, and the functional capacity to use AI to enhance their own daily workflow, while understanding the ethical implications and without diminishing their ownership of the work completed.

Humboldt's “Applied Storytelling" is a new multidisciplinary Critical Thinking (Area 1B) course that is focused on the methods of inductive and deductive storytelling, positioning storytelling as both a pedagogical tool and a career-relevant competency.

Throughout, the course guides students to employ AI tools to maximize their own learning and to begin to explore how this emergent technology might best support their own professional goals. This project and the funding associated with it has allowed us to experiment with the emergent technology of AI in ways and with a focus that would not otherwise have been possible.

This project's co-PIs were already inclined to understand AI, because our students were using it with increasing frequency; with the dedicated time allotted through this project, we have focused on educating ourselves around AI ethics and best practices, pedagogical techniques, and workforce trends. By far, though, the most valuable time that we have spent has been in experimentation.

The AIEIC project has granted us the time to play around within a technological space that was previously unknown to us. Upon reflection, we can say that not all of our time was well spent, in that not all of our ideas came to meaningful fruition. Even time that turned out not to be