Tag: AI Tools

Ask an ID: Redesigning Music Essay Assignments in the Age of AI

Post author By sharris
Post date July 16, 2026

Ask an ID: Redesigning Music Essay Assignments in the Age of AI

Dear Instructional Designer,

I’m redesigning a General Education Music course. One of the major assignments is a traditional concert analysis essay where students analyze several musical works using course vocabulary. With generative AI, it feels increasingly difficult to know whether the final paper reflects the student’s own thinking. How can I redesign this assignment so it still meets the learning objectives while making student learning more visible?

-Unplugged Educator

Dear Unplugged Educator,

One of the biggest questions instructional designers are hearing right now isn’t whether students can use AI. It’s how we design assignments that still reveal what students know, notice, and understand. The good news is that the answer usually isn’t finding a better way to detect AI. It’s designing assessments that make thinking visible.

Traditional essays have long been a staple of higher education because they ask students to synthesize ideas, communicate clearly, and demonstrate understanding. The challenge today is that large language models have become remarkably good at producing polished essays. While those essays may sound convincing, they often hide the very thing instructors are trying to assess: the student’s own observations, reasoning, and growth.

That doesn’t mean essays have no place. It does mean we should ask an important question:

Is the essay the learning, or is it simply one way of communicating learning?

The AI-Responsive Assignment Design (ARAD) framework encourages us to shift our focus from protecting assignments against AI to designing assessments that make students’ thinking, decision-making, and learning process visible. Rather than relying on a single polished product, ARAD emphasizes authentic tasks, iterative thinking, reflection, and disciplinary practice.

For this General Education Music assignment, the learning objectives aren’t really about writing essays. They’re about learning to listen critically, recognize musical styles, analyze musical elements, use disciplinary vocabulary, evaluate performances, and reflect on personal growth as a listener.

With those goals in mind, here are several redesign options that preserve the learning while creating richer evidence of student thinking.

🎥 Reaction Videos

Instead of writing about music after listening, students record themselves listening in real time. They pause at key moments to explain what they notice, using musical vocabulary and timestamps to support their observations.

Students then watch classmates’ videos and extend one another’s interpretations with additional evidence from the performance.

Why I like it: This design captures authentic listening, encourages evidence-based discussion, and helps students see that musical interpretation is strengthened through dialogue rather than isolated writing. It also uses a popular format, the reaction video, that students might already be familiar with and maybe they will share it with a broader audience.

🌐 Discover This Composer Website

Students create a simple website that introduces one or more composers to a general audience. Rather than writing only for the instructor, they curate an experience for future listeners.

Pages include:

embedded performances
composer background
musical observations
listening tips
timestamps
course vocabulary
connections to similar artists

Why I like it: The assignment remains analytical but becomes authentic. Students are communicating with a real audience, not simply completing a paper for a grade. This could even become a portfolio piece and be paired with one of the Micro-credential courses like Interactive Media.The website becomes a learning artifact that the student will have to demonstrate multiple skills to accomplish while also meeting the same requirements as the original essay.

💬 AI Concert Companion (Blackboard AI Conversation)

Students participate in a Blackboard AI Conversation while actively listening to assigned musical works. The AI does not analyze the music for them. Instead, it continually asks questions that prompt observation, analysis, evaluation, and reflection.

For example:

What musical evidence supports your interpretation?
Which musical element influenced your reaction?
Can you identify a timestamp that illustrates your point?
Has your interpretation changed after listening again?

Students then use that conversation to inform a final reflection or analysis.

Looking at the objectives, here’s how we could frame the discussion.

Learning Objective	Blackboard AI Conversation Purpose
Identify the work	Prompt students to explain the title, composer/songwriter, artist, and style in their own words.
Determine musical styles	Ask students to compare stylistic characteristics and justify their conclusions.
Analyze musical elements	Probe for evidence using melody, rhythm, texture, timbre, harmony, dynamics, form, etc.
Use musical vocabulary	Encourage students to replace everyday language with disciplinary terminology.
Evaluate performance	Ask students to critique interpretive choices using evidence.
Reflect on listening	Prompt metacognitive reflection on how their listening has changed since the beginning of the course.

Why I like it: The AI becomes a scaffold for disciplinary thinking rather than a shortcut to completing the assignment. The conversation also creates valuable evidence of the student’s learning process. I think you could add this Blackboard AI conversation into any of the other ideas and have the student start there. Or you could have this and pair it with a reflective essay,

This conversation is essentially guided formative assessment. It functions as a scaffolded inquiry that moves students through increasingly sophisticated levels of thinking.

For example, using a progression inspired by Bloom’s Taxonomy:

Remember: What instruments or voices do you hear?
Understand: How would you describe the musical style?
Apply: Which musical terms best describe this section?
Analyze: Which musical elements contribute most to the mood? How do they work together?
Evaluate: How effective is the performance? What evidence supports your evaluation?
Reflect/Create: How has this course changed the way you listen to music? How will you communicate these insights to your audience?

Or, even better for a music appreciation course, you could structure the AI around the disciplinary practice of close listening:

Observe: What do you hear? (Description without judgment)
Interpret: What might these musical choices communicate?
Support: What evidence from the recording supports your interpretation?
Evaluate: How effectively does the performance communicate those ideas?
Reflect: How has your understanding changed through repeated listening?

That progression closely mirrors how music scholars and critics actually approach musical works. It also aligns naturally with the assignment’s objectives and positions the Blackboard AI Conversation as a scaffold for disciplinary thinking rather than a tool for generating a final product.

🤖 Compare Your Listening to AI’s Listening

Students first write their own observations before asking an AI tool to analyze the same musical work.

They then compare the two analyses by identifying:

similarities
differences
what the AI overlooked
which interpretation is better supported by evidence

Why I like it: Instead of outsourcing thinking, students practice evaluating AI critically. They learn that AI outputs are starting points for analysis, not authoritative answers.

🎼 Curate a Concert for a Specific Audience

Rather than selecting works simply because the assignment requires them, students curate a concert around a meaningful purpose.

Examples include:

introducing classical music to beginners
celebrating cultural identity
music for resilience or healing
dance traditions across cultures

Students justify every programming decision using evidence from the music.

Why I like it: This moves students beyond summarizing individual works toward synthesis, one of the highest levels of cognitive learning. They must think like curators rather than reporters. Also, taking something that you understand and reframing it for another audience is probably the best way to truly capture learning. If we translate the material it helps our brains realize if there are holes in the logic when we explain it to someone else.

Designing for Thinking, Not Detection

None of these redesigns eliminate writing. Instead, they broaden the ways students can demonstrate learning.

In the music department, their learning objectives also include an essay writing component so it’s easy to add a reflective essay about their learning process that can demonstrate their learning and provide that metacognitive reflection that’s so important.

Whether students are creating reaction videos, designing museum exhibits, engaging in AI-supported listening conversations, or curating concerts, the emphasis stays where it belongs: on careful observation, disciplinary reasoning, evidence, and reflection.

That’s ultimately the goal of AI-responsive assignment design. Rather than asking, “How do we stop students from using AI?” we can ask a more productive question:

“What kinds of learning experiences make student thinking impossible to hide?”

I hope this is helpful! Please don’t hesitate to reach out if you have any additional questions.

To orchestrating student success,

Your Instructional Designer

Tags AI, AI Tools, Ask an ID, Student Learning

Feature

AI@ACC Panel 4: Assessment in the Age of AI

Post author By sharris
Post date April 27, 2026

AI@ACC Panel 4: Assessment in the Age of AI

AI@ACC Panel 4 featured LaKisha Barrett, Sajjad Mohsin, Dania Dwyer, Sara Farr, Jaime Cantu, Marian Moore, and Ronald Johnson in a conversation on rethinking assessment in the age of AI, highlighting approaches that make student thinking visible and learning more meaningful.

Rethinking Assessment in the Age of AI

At the final AI@ACC panel, we kept coming back to one simple question: if AI can do the assignment, what are we actually assessing?

What stood out right away was that the conversation didn’t feel like a crisis. No one was arguing that everything is broken or that we need to start over. Instead, what came through was something more grounded and, honestly, more encouraging. Faculty are already adjusting. And in many cases, they’re moving toward approaches that research has been pointing to for decades, especially around authentic assessment and deeper learning.

When people talk about “authentic assessment,” it can sound abstract. But during this panel, it showed up in very concrete ways.

Jaime Cantú in biology described having students explain complex concepts to different audiences like athletes, patients, or children. That kind of task immediately raises the bar. Students can’t rely on memorization because they have to actually understand the material well enough to translate it. In learning science, this kind of transfer, taking knowledge and applying it in a new context, is one of the strongest indicators of deep understanding (see How People Learn by the National Research Council). Jaime has also been experimenting with AI tools that surface student thinking and even reward students for asking good questions, not just giving correct answers. Read more of Jaime’s research on assessment with Blackboard AI Conversation tool. That shift toward valuing inquiry aligns closely with research on metacognition and self-regulated learning (see Barry Zimmerman’s work on self-regulated learning).

In game design, Sara Farr described a different kind of assignment, but one that gets at the same core idea. Her students are creating original work, building games, visuals, and narratives, and documenting how those ideas evolve over time. The final product matters, but so does the process and the decisions behind it. Students are asked to show how their ideas developed, why they made certain choices, and how they refined their work. This kind of iterative, design-based learning reflects what Grant Wiggins describes as authentic assessment, where students are asked to produce work that mirrors real-world performance and requires judgment, not just correctness.

Dania Dwyer in composition is taking a more explicitly AI-integrated approach, but in a very intentional way. She allows students to use AI as part of their writing process, but they are still responsible for shaping the argument, making rhetorical choices, and explaining their decisions. She shared that she has been genuinely impressed with the quality of student work when AI is used thoughtfully. What she is really assessing is how students develop and refine ideas over time. That emphasis on writing as a process, not just a product, is well supported in research on learning, including work synthesized in How Learning Works by Susan Ambrose and colleagues.

In computer science, Dr. Sajjad Mohsin described a shift that feels especially relevant in the age of AI. Instead of grading only whether code works, he asks students to document their entire process through logbooks. Students explain how they approached a problem, how they used AI to troubleshoot, what prompts they tried, and how they worked through errors. They also have to explain exactly what their code is doing and why. This makes their thinking visible in a way that a finished program never could. It also aligns with research on cognitive apprenticeship and making thinking visible, such as the work of Allan Collins and colleagues.

When you put these together, the assignments look very different on the surface, but they’re all getting at the same thing. They’re asking students to apply what they know, explain their reasoning, make decisions, show their process and create something original.

The Mentimeter responses from participants reinforced this. When asked what critical thinking looks like, people described things like evaluating AI outputs, reflecting on their learning, and applying knowledge in new contexts. That’s notable because it shows that AI is already being folded into how faculty understand thinking itself. At the same time, when asked what their assessments currently reward, creativity came in lowest. That gap is important. It suggests that while many faculty are already valuing explanation and reasoning, there is still room to expand how we assess originality and generative work, something that becomes even more important when AI can produce polished outputs so easily.

At the same time, when asked what their assessments currently reward, creativity came in lowest. That gap is interesting. It suggests that while many of us are already valuing explanation and reasoning, there’s still room to expand how we assess originality and generative work.

One comment that we heard a lot was someone saying that “text homework done at home is basically useless now.” That might feel a little blunt, but it points to something real. Some kinds of assignments are becoming less reliable as evidence of learning. But that doesn’t mean everything is falling apart. It aligns with long-standing research on assessment validity, including work by Samuel Messick, which emphasizes that assessment must be continuously re-evaluated as contexts change.

The biggest takeaway from the panel is that we’re not starting from scratch. Faculty like Jaime, Sara, Dania, and Sajjad are already showing what this can look like in practice. They’re designing assignments that make thinking visible, even when AI is part of the process.

AI is definitely changing what students can produce. But it’s also pushing us to get clearer about what we actually care about. If we care about understanding, reasoning, and the ability to use knowledge in meaningful ways, then our assessments need to reflect that.

And in many cases, they already are.

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.

Tags AI, AI Tools, AI@ACC, Assessment, Student Learning

Feature

AI@ACC Panel 3: AI@Work: Faculty and Industry Perspectives

Post author By sharris
Post date April 2, 2026

AI@ACC Panel 3: AI@Work: Faculty and Industry Perspectives

AI@ACC Panel 3 featured Beth Vaughn, Gwen Holford, Lani Dame, and Jennifer Houlihan in a lively conversation about how AI is reshaping workforce expectations and the skills our students need next. Mentimeter responses highlighted critical thinking, adaptability, and communication as key strengths.

What AI@Work Means for the Classroom: Takeaways from Panel 3

At a recent AI@ACC panel, colleagues from across the college — workforce training, healthcare partnerships, instructional technology, and career development — sat down to talk honestly about AI and work. The conversation was candid, sometimes surprising, and really useful for anyone who teaches or supports learners here.

Here are some takeaways (and you can watch the whole panel here).

Employers aren’t waiting for us to catch up

Panelists Jennifer Houlihan, Beth Vaughn, Gwen Holford, and Lani Dame were consistent on one point: AI fluency is already an expectation in many workplaces. Organizations aren’t debating whether AI will change jobs — they’re actively redesigning workflows and looking for employees who can navigate that shift. Tasks like documentation, summarization, and research are changing fast. The human stuff like judgment, communication, and relationship-building isn’t going anywhere.

For those of us designing courses and learning experiences, that’s a meaningful signal.

Our colleagues are already using AI, too

A live Mentimeter poll during the session confirmed what many of us probably already suspected: most participants are already using AI tools regularly — for course development, lesson planning, writing, and pulling together information. This isn’t a future-tense issue. It’s happening in our offices and classrooms right now.

What should we prioritize? The audience was clear.

When participants were asked what higher education should focus on as AI reshapes work, the responses landed in a familiar place:

Critical thinking and reasoning — ranked highest
Ethical decision-making — a close second
Technical AI skills — important, but not the whole picture
Communication, adaptability, collaboration, and lifelong learning — still very much on the list

One framing from that I think is importatnt: the goal is AI-augmented people, not people replaced by AI. Students need to become strong communicators, sound decision-makers, and adaptable thinkers who can work alongside both human and AI collaborators.

The concerns are real, and worth designing around

Participants also shared genuine worries — and I think these deserve space in how we approach course design:

Over-reliance on AI tools at the expense of real thinking
Students outsourcing judgment rather than developing it
Declining critical thinking and interpersonal skills
Implementing AI without a clear sense of the problem it’s solving
Bias and fairness in AI systems

These aren’t abstract concerns. They’re design challenges. How do we build learning experiences that use AI intentionally, while still helping students develop the thinking and judgment that AI can’t replace?

The skills haven’t changed — the stakes have

When the panel closed with a question about what will help students thrive, the room answered with: critical thinking, adaptability, communication, curiosity, resilience, and open-mindedness.

Sound familiar? These are the outcomes we’ve always cared about. AI is changing the tools and the context, but it hasn’t changed what it means to be a capable, thoughtful human being in the world.

That’s actually encouraging. It means the work we’re already doing matters — and we have a real opportunity to do it even more intentionally as AI becomes part of everyday learning and work.

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

Tags AI, AI Tools, AI@ACC, Student Learning

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Ask an ID: Updating Course Materials for Accessibility

Post author By sharris
Post date March 26, 2026

Ask an ID: Updating Course Materials for Accessibility

Dear Instructional Designer,

I’ve been using the same Google Slides and scanned PDFs for years, but I’m realizing they probably aren’t accessible for all my students. Between my slide decks and these old documents, the task of updating everything feels overwhelming and I don’t even know where to begin. Do you have any advice or tools for a non-tech expert to help me get my existing course materials up to current standards?

– Accessibly Anxious

Dear Accessibly Anxious,

It’s completely normal to feel overwhelmed by the technical side of accessibility, but you don’t have to become an expert overnight to make a big impact. Here is a curated roadmap of tools and workflows to help you systematically bring your slides and documents up to current accessibility standards.

1. Audit materials with Blackboard Ally

The Accessibility Report on Blackboard Ally is a great place to find out what is flagged in your existing documents. Here’s a help document from the University of Arkansas that goes through the steps to working with Ally. We also did this Blackboard workshop a couple of years ago that talks about Ally and how to use AI to write alt-text for images and help with captioning if you have videos.

2. Making Slide Decks Accessible

When you are ready to remediate your slides for screen readers, the process depends on the tool you used to create them. Here are the go-to guides for the most common platforms:

Google Slides: One-Page Faculty Guide for Screen Readers
Canva: Using Accessibility Features and Alt-Text

3. PDF Accessibility

When it comes to PDFs, it is almost always easier to return to the original source file. Research shows that starting with an accessible MS Word or Google Doc produces far more reliable results than trying to “fix” a document inside Adobe Acrobat.

If you don’t have the original source file, you can still use the Adobe Acrobat Accessibility Checker and the Reading Order tool. (You can access your free ACC Adobe Creative Cloud subscription here). For step-by-step guidance, I recommend:

PopeTech Tutorials: These offer clear video and written instructions on tagging and checking for common issues. They have excellent guides on How to check and fix PDF accessibility issues, When to use HTML instead of PDFs, and here is a great video on How to use the Fix Reading Order tool.
The Accessibility Guy: This site provides short, skill-specific tutorials that are perfect for those who are already fairly comfortable with technology. Here is the guide to PDF Accessibility.

4. Looking Forward: AI and Design

Since we are now designing courses in the “AI era,” it’s helpful to use a framework like AI-Responsive Assignment Design (ARAD). This approach helps you create assignments that are both accessible and ethically aligned with current technology.

General Resources for Your Toolkit

WCAG Guidelines: The global standard for Web Content Accessibility Guidelines.
Authoring Meaningful Alternative Text: A great resource for learning how to Write Effective Alt-Text.
AI Alt-Text Generators: Tools like the Arizona AI Alt-Text Generator can give you a head start on describing complex images.

I know it’s a lot, but try to take it one step at a time. The best part? When you start building with accessibility in mind, you won’t have to go back and “fix” things later—you’re just doing it right the first time.

Good luck! Let me know if I can be of further assistance.

Yours in inclusion,

Your Instructional Designer

Tags Accessibility, Adobe Creative Cloud, AI, AI Tools, Ask an ID, Blackboard Ally, Student Learning

Feature

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

Post author By sharris
Post date March 5, 2026

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!

Tags AI, AI Tools, AI@ACC, Student Learning

Faculty White Papers

Mastering the Art of Rehearsal: Utilizing Digital Tools for Enhanced Speech Delivery and Self-Assessment

Post author By sharris
Post date February 26, 2026

Learn how “LMC” Lisa Marie Coppoletta transformed her students’ public speaking anxiety to professional confidence in her Speech Communication course. This white paper details how structured preparation and collaborative reflection fosters confidence and produces polished presentations.

READ

Tags Adobe, Adobe Creative Cloud, AI, AI Tools, Padlet, Student Learning

Faculty White Papers

Redesigning ACNT 2330 with AI-Enhanced Exam Preparation

Post author By sharris
Post date February 19, 2026

Discover how Okera Bishop utilized an AI-powered tutor to support exam preparation in an aysnchronous accounting course. This white paper details how this approach increased student confidence, eliminated failing exam scores, and reduced score variability, highlighting AI’s value for targeted practice.

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Tags AI, AI Tools, Student Learning

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AI@ACC Panel 1: Talking to your Students about AI Ethics

Post author By sharris
Post date February 12, 2026

AI@ACC Panel 1: Talking to your Students about AI Ethics

AI@ACC Panel 1 brought together Alex Watkins, Toño Ramírez, Andy Kim, and Mavis Klemcke for a candid, laugh-a-little conversation about syllabus policies, student fears, and what transparency really means. Mentimeter results showed big love for openness and process over policing.

AI@ACC Panel 1 Recap: Talking to Your Students About AI Ethics

The first AI@ACC panel opened with a big question many faculty and staff are asking right now: How do we talk to students about AI in ways that are thoughtful, ethical, and practical? The conversation brought together Alex Watkins (Technical Communications and AI Committee), Tono Ramírez (Philosophy and AI Committee), Andy Kim (Energy and Sustainability), and Mavis Klemcke (Faculty Librarian), with moderation focused on creating space for open conversation and shared learning around AI at ACC.

One thing became clear quickly. People across the college are in very different places with AI. Mentimeter responses showed that most attendees described themselves as experimenting or actively engaging with AI conversations, with only a small number avoiding or observing from the sidelines.

The discussion centered on a message that came up repeatedly: students need guidance, not silence. Participants strongly agreed that ignoring AI risks leaving students unprepared for civic and professional life. They also strongly agreed that ethical reasoning about AI is a skill students will carry into careers beyond college.

Transparency matters

One of the strongest themes from both the panel and Mentimeter responses was the importance of transparency.

Participants overwhelmingly agreed that:

Students benefit when faculty explain the reasoning behind AI boundaries
Open conversations about AI can reduce student anxiety
Students deserve transparency about how instructors use AI
Students need guidance about why to use AI responsibly, not just rules about whether they can use it

Faculty concerns also surfaced clearly. When asked what student situations felt hardest to navigate, the top challenge by far was determining what to do when it seems obvious a student did not write their own paper. Other challenges included inconsistent expectations across courses and questions from students about where the line between support tools and cheating exists.

Process matters more than detection

A powerful thread throughout the discussion focused on moving beyond trying to “catch AI” and instead designing learning experiences that make thinking visible.

Faculty shared strategies like:

Breaking writing into smaller stages with drafts and revisions
Using peer review and process feedback
Looking at revision histories
Building reflective components into assignments
Focusing on how students arrived at answers, not only final products

Mentimeter responses reinforced this idea. Participants strongly agreed that teaching with AI requires rethinking assignments, not simply policing behavior.

AI literacy belongs across disciplines

Another interesting finding involved AI literacy itself.

Participants leaned strongly toward the idea that AI-generated content should be treated as something to interrogate and think critically about rather than automatically trust. AI literacy was framed not as a technology issue alone, but as a cross-disciplinary skill involving critical thinking, ethics, information literacy, and judgment.

The panel also highlighted practical concerns faculty and staff face every day:

Which AI tools are institutionally supported
Privacy considerations
Responsible use of third-party tools
Understanding policies around data sharing and approved technologies

Panelists encouraged caution, thoughtful experimentation, and attention to institutional guidance while recognizing that AI technologies are evolving rapidly.

What stood out most

Perhaps the strongest takeaway from Panel 1 was that faculty and staff do not need perfect answers before beginning conversations.

The goal is not to become an AI expert overnight.

The goal is to create classrooms and workplaces where students can ask questions, think critically, wrestle with ethical challenges, and learn how to navigate technologies they will encounter long after leaving ACC.

As one theme surfaced repeatedly throughout the session: AI conversations are no longer optional. They are becoming part of helping students prepare for the future.

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

Tags AI, AI Tools, AI@ACC, Student Learning

Faculty White Papers

AI-Powered Chatbot Assessments in Online Anatomy & Physiology: A Mixed-Methods Study White Paper

Post author By sharris
Post date February 6, 2026

Follow along as Jaime Cantú compares traditional tests with AI chatbot assessments in his online Anatomy & Physiology courses. This white paper reveals how chatbots can foster deeper understanding, stronger explanation skills, and lower test anxiety through patient-centered dialogue.

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Tags AI, AI Tools, Student Learning

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Ask an ID: Backward Design: Rethinking Curriculum for Accelerated Sessions

Post author By sharris
Post date January 29, 2026

Ask an ID: Rethinking Curriculum for Accelerated Sessions

Dear Instructional Designer,

I am an Adjunct Professor who has been at ACC for 20 years. I have always taught a 16-week course but I was just assigned a 12-week session for the first time. I am unsure how to best approach and manage this new session given the difference in length and would appreciate any guidance you can provide.

– Course Compressor

Dear Course Compressor,

I can certainly understand how this shift in course length presents a new challenge. Not to worry – here are some tips and resources to help you make this adjustment a smooth one for both you and your students.

Backward Design is how we usually look at course structure. We start with the learning outcomes, what we want the students to be able to demonstrate, and then figure out the materials, activities, and assessments that go with each objective. You can access an example ACC course map here, which I encourage you to fill out. You want to look at your 16 week course objectives and figure out what is absolutely required, what is important, and what is just “nice to know” and cut out some of those. Trying to fully condense a 16 week into 12 without cutting anything isn’t generally recommended because of cognitive load theory and the spacing effect.

Rethink if you want to move from weekly modules to “unit” modules. A typical pattern is to fold Weeks 1–2, 3–4, 5–6, etc., into combined modules with clearer themes.

Be clear to the students about what is going on. Explain that they are in an accelerated course so things are going to move faster than they may be used to. Remind the students that it will be imperative to stay on top of their work and outline in every module what they need to Read or Watch, what they need to Do, and what the Assessment will be. Connect the learning outcomes to those activities to help them understand why they are doing what they are doing. And give them a ballpark figure of how much time you are expecting each chunk to take. I like to create a PDF course schedule with the dates of the term and all the due dates so that they can print it or save it and cross things off.

You also want to use frequent, smaller check‑ins (quick quizzes, minute papers, short reflections) to monitor learning and catch problems early when things move faster. This is especially important because students in shortened terms can experience more stress and less recovery time between tasks.

It’s a lot to generate so I recommend leaning on Google Gemini for help. It’s in your Google Workspace Tools. Make sure you use your ACC linked account so that all of your course materials will stay secure and make sure to double check every single thing it generates for students because there can always be hallucinations and errors with generative AI.

I hope this helps you! Please don’t hesitate to reach out with any further questions or to set up a 1:1 meeting.

Backwards by design,

Your Instructional Designer

Additional Resources:

Achieving the Dream Preparing Shortened Academic Terms (page 20-22)

Planning a Compressed Course Montclair State

Six effective ways to teach accelerated courses

Tags AI, AI Tools, Ask an ID, Google Gemini, Student Learning