AI@ACC Panel 2: Reimagining Curriculum for the AI Era

AI@ACC Panel 2 featured Herb Coleman, Janey Flanagan, Susan Meigs, and Tina Buck in a thoughtful, energetic redesign chat where we dug into the new Blackboard AI Conversations tool. Mentimeter showed strong support for AI disclosure and clearer goals, with “everyone” claiming AI literacy as shared work.

AI@ACC Panel 2: Reimagining Curriculum for the AI Era

What happens when artificial intelligence changes not only how students complete assignments, but how we think about teaching itself?

That was the focus of AI@ACC Panel 2, Reimagining Curriculum for the AI Era, a thoughtful conversation featuring Herb Coleman (Psychology), Janey Flanagan (Faculty Center for Learning Innovation), Susan Meigs (Composition and Literary Studies), and Tina Buck (English and instructional design). The discussion moved beyond questions of detection and policy and into a deeper challenge: How do we design learning experiences that still matter in a world where AI exists?

The panel brought together faculty and staff from across disciplines including math, humanities, instructional design, nursing, chemistry, library services, engineering technology, health sciences, communication studies, and composition, highlighting that AI is no longer a niche conversation. It touches nearly every area of the college.

Curriculum redesign is becoming curriculum transformation

One of the clearest themes was that many faculty are not simply tweaking assignments. They are rethinking courses from the ground up.

Susan Meigs shared that after returning to teach Composition II, she realized her previous course design was “too easy to game” in an AI-enabled world. Rather than making small adjustments, she redesigned the entire course. Instead of focusing narrowly on elements of short fiction, students now explore themes around the pursuit of happiness through multiple texts and experiential learning activities. She also intentionally teaches AI literacy, embeds AI-related learning opportunities, and facilitates classroom discussions about ethical and unethical AI use.

That willingness to redesign rather than defend older structures echoed throughout the panel.

Mentimeter responses reinforced this shift. When participants ranked course design changes they have prioritized since generative AI became widely available, the top responses included:

AI use disclosure
Encouraging students to critique AI outputs
Making learning goals more explicit
Building visible thinking into assignments
Adding structured draft checkpoints
Requiring reflection on AI use
Modeling responsible AI use in class
Integrating AI as a conversation partner

The message was clear: faculty are increasingly focusing less on preventing AI use and more on designing learning experiences where thinking, reasoning, and growth remain visible.

Process matters more than ever

One of the strongest ideas to emerge from both the panel and participant responses was the growing emphasis on process over product.

Mentimeter participants ranked “ability to transfer knowledge” as the strongest signal of authentic understanding, followed by contextual examples and clear reasoning. Personal voice, revision over time, peer dialogue, and oral explanation also mattered.

Participants also responded to questions about assignments and learning processes:

Many leaned toward requiring process requirements alongside traditional assignments.
Participants generally agreed that if students cannot explain their thinking orally, learning may not be fully developed.
When asked what matters most in an exceptional final product, alignment with learning goals and students’ explanation of reasoning ranked above factors like polish or documentation alone.

These findings align with a growing shift in higher education: designing assignments that reveal how students think, revise, connect ideas, and apply learning.

Students still want human connection

One particularly interesting insight came from student perspectives.

Janey Flanagan shared findings from student feedback indicating that students appreciated AI tools like chatbots for support outside instructor working hours. One student specifically valued being able to study late at night and receive immediate answers when faculty understandably were unavailable. AI provided helpful scaffolding and support.

But students also expressed an important boundary.

They overwhelmingly said they did not want AI grading their work. Students wanted instructor feedback, critique, and authentic human engagement with their learning. As one panelist summarized: students do not want to submit AI-generated work, and they also do not want AI evaluating it.

That reminder may be one of the most important takeaways from the conversation.

Even as AI tools become more integrated into teaching and learning, students still value expertise, mentorship, and genuine human feedback.

AI literacy belongs to everyone

One Mentimeter question asked:

“Whose responsibility is it to teach AI literacy?”

The participant responses created a remarkably clear picture.

The dominant answer was simple:

Everyone.

Words like “everyone,” “all of us,” and “ours” filled the screen.

That idea also surfaced throughout the panel discussion. Faculty discussed helping students understand not only how to use AI tools, but when to use them, how to evaluate outputs critically, and how to think ethically about AI-supported work. Susan Meigs described embedding AI literacy directly into coursework so students can develop informed decision-making rather than simply tool familiarity.

What students may misunderstand about AI and learning

Participant responses highlighted several concerns faculty and staff are seeing:

Students focusing on products rather than learning processes
Overvaluing speed and shortcuts
Assuming AI “understands” rather than generates patterns
Missing opportunities to build writing, reasoning, and problem-solving skills
Accepting AI output without sufficient evaluation
Losing sight of the value of developing expertise over time

One response captured the tension especially well:

“Whoever does the work, does the learning.”

That idea sat at the center of the entire conversation.

Key takeaways for faculty and staff

If there was one overarching message from Panel 2, it was this:

AI is not only changing tools. It is changing instructional design.

Some practical ideas that emerged:

Design assignments that emphasize reasoning, transfer, and explanation
Make learning goals more explicit
Build reflection and visible thinking into coursework
Help students critically evaluate AI outputs
Teach ethical AI use directly rather than assuming students understand it
Consider where AI can support learning without replacing it
Preserve opportunities for authentic human feedback and connection

Reimagining curriculum does not mean abandoning what works. It means asking deeper questions about what learning should look like now.

And judging by this panel conversation, ACC faculty and staff are already doing that work thoughtfully, collaboratively, and with students at the center.

View the session summary or watch the session recording to dive in deeper!

AI@ACC Panel Series is a four-part, cross-disciplinary, dialog-based conversation series developed through Austin Community College’s (ACC) participation in the AAC&U Institute on AI, Pedagogy, and the Curriculum. Grounded in national research, the series explores how artificial intelligence is shaping teaching, learning, assessment, and the future of work in higher education across teaching, support, and workforce roles.

Designed as a low-pressure entry point, this series centers real questions, lived experience, and diverse perspectives rather than tools, mandates, or hype. Ethical concerns, including bias, labor, environmental impact, and academic integrity, are acknowledged and respected throughout. No prior AI experience is expected. Questions and uncertainty are welcomed.

AI@ACC is a space for inquiry, not compliance. The series is exploratory and reflective rather than directive. While AI raises serious concerns, disengagement does not ultimately protect students. These conversations focus on helping educators and staff thoughtfully support students as they navigate evolving academic and workplace norms.