INTRODUCTION

A Note About the Project’s Structure

The ADDIE model — Analyze, Design, Develop, Implement, Evaluate — was used as my framework. ADDIE is often considered a linear sequence, but the truth for me is that it loops about and often doubles back on itself. In its later iterations, this project's evaluation and feedback provided inspiration for a new design cycle and instructional opportunities.

While ADDIE structured how I built the project, Bloom's Taxonomy structured what it asks of the learner. The design is built as a scaffolded progression through the cognitive levels: from comprehension (understanding how AI works), through evaluation (judging a specific tool against one's own values and community), to creation (authoring a personal ethical code the learner owns). The learning outcomes are a move from basic understanding to original synthesis, with the personal ethics-code activity as its top rung.

Project Snapshot

• My Role: Originator, Lead Designer & Co-Facilitator

• Collaboration: Cross-departmental stakeholder input — library, writing center, education department, instructional technology. I conceived the symposium proposal, convened the partners, and executed the instructional design.

• Audience: Full campus community — students, faculty, and staff across all degree levels and programs (distributed / remote)

  Format: Option A: Facilitated Workshop, 50–60 min. Option B: Workshop plus self-selected breakout discussions (Symposium),

  90–105 min.

• Tools: PowerPoint · Google Docs · Google Forms · Trello · Canva

• Deliverables: Slide deck · facilitator guide · breakout questions · reflective handout · code-of-conduct assignment · resource list

Problem and Opportunity

This workshop developed as a response to student and faculty concern over the appropriate use of AI in scholarly settings. The opportunity I saw was to guide students towards applying their own ethical criteria, develop informed choices about AI, and define personal boundaries around using AI.

• Context: a community navigating AI with no shared norms and no shared vocabulary.

• Tension: rising anxiety alongside uncritical adoption, and disagreement among faculty about best practices.

• Timing: before any university AI policy had been formalized; department-level guidance was vague or confusing

These sessions were designed to open a dialogue, not to advocate for or advise against the use of AI. I hoped to create a safe space for students to explore their questions and concerns, and provide a framework for their own ethical decision-making.

Audience and Context

• Who: students, faculty, and staff with a wide range of AI fluency and comfort.

• Setting: a distributed / remote university; sessions run live virtually with opportunity for an asynchronous recorded version.

• Constraints: in-house production tools only (Microsoft and Google); voluntary attendance.

Four learner-characteristic areas were considered in identifying both the audience and the context in which they might be most receptive to information:

• Cognitive: a mixed level of fluencies and comfort with AI

• Affective: strong emotionally driven opinions covering the spectrum from dislike to anxiety to excitement

• Social: ensuring content met cross-disciplinary and cross-departmental needs; providing a safe space for open discussion

• Physiological: geographically distributed, fully remote attendees, multiple time zones

ANALYSIS

Method

I developed a nine-question faculty survey covering four areas: current experience and comfort with AI tools; current and prospective use of Gen AI to build instructional and assessment materials; views on student use; and interest in AI training by type and delivery format. I distributed and captured responses through Google Forms for the primary survey group, inter-departmental faculty and staff. With my library colleagues I conducted live interviews; combining the data from both allowed for different perspectives and feedback sensitivity. Twenty-one faculty and staff were invited and I received nineteen responses, reflecting an effectiveness in the interview-plus-form approach.

As part of scoping the effort, I also used informal conversations and email input from students who were bringing the topic need up to me and my colleagues. Using the survey, I found interested collaborators for cross-departmental consultation, inviting faculty and staff from the library, writing center, the education department, and instructional technology. The needs analysis drew on formal survey data, student input, and collaborator input.

Key Survey Findings

● Comfort and usage clustered low-to-moderate (roughly 3–5 on a 0–10 scale).

● Top concerns: accuracy and hallucinations, ethics and academic integrity, environmental cost, skill degradation, and student voice.

● Limited use of AI in course development; a majority of faculty discouraged or banned student use in coursework.

● Strong appetite for hands-on, context-specific training; preferred formats were in-house workshops and asynchronous modules.

● The library's role was seen as supportive and collaborative, a signal I honored through my design and delivery formats

From Finding to Design

The survey revealed that caution and misapprehension about AI were driving avoidance and outright bans among faculty, even as they asked clearly for student training. The finding that students were often furtive or uninformed in their AI use, along with the inconsistent guidance, pointed at a approach (see more in §6). My decision was to design a flexible workshop around a foundational summary of how AI tools work, what they are good for, and where they fail — one that flexes across settings (classroom, stand-alone workshop, multi-presenter seminar) and timings (50 to 100+ minutes).Community of Inquiry

Design Decisions and Rationale

Facilitated dialogue over lecture.

I chose to run the session as facilitated dialogue rather than a lecture, for two reasons my analysis made clear. First, the faculty survey showed respondents saw the library's role as supportive and collaborative, not directive, and a lecture would have contradicted the relationship they were asking for. Second, the subject matter demanded it: AI ethics carries not just many viewpoints but strong emotional ties to those viewpoints, and I felt it was inappropriate to lecture someone toward a personal ethical stance. This design reflects the Community of Inquiry framework, in which learning emerges through the interaction of social, cognitive, and teaching presence rather than content delivery — and Brookfield and Preskill's case for discussion as a way of teaching from Discussion as a Way of Teaching, which holds that contested questions are better navigated by reasoning together than by transmission.

A personal code of ethics over a top-down mandate.

In the absence of a University-wide AI policy, I designed the session to guide each participant toward their own code of ethics. The reasoning was guided by three learning theories, starting with practical: the world will ask people to make AI decisions at many levels, and after graduation there is often no policy, instructor, or easy guidance to lean on — each person has to know their own boundaries. A mandate would also have undercut the developmental goal, which was to build the capacity to reason about AI use, not compliance with someone else's ruling. Contextually, the real vacuum in policy required individuals from all sides to make their own informed decisions. This design also rests on a constructivist premise that each learner builds a durable ethical framework by integrating what they know with their own reflection and values, rather than receiving a position secondhand. The "Decide For Yourself" framing asks the learner to be the author and develop an evolving judgment of their AI use that survives the workshop.

Six self-selected topic rooms.

Based on our collaborative sessions, we decided on six breakout rooms and let each participant choose which two to join. The principle behind it is one I keep close: honoring the learner's voice through learner's choice. Drawing on Self-Determination Theory by leaning into the concept that intrinsic motivation depends on autonomy, competence, and relatedness, this self-selection satisfies autonomy directly, clusters learners around a shared concern, and promotes relatedness. I curated the six rooms from the concerns my analysis and our collaborative sessions surfaced with feedback from the group.

A reflective take-home handout.

A single session provokes questions but rarely settles them; I built the reflective handout "Guided Considerations Toward a Personal Code of Ethics" for participants to continue their internal process. It extends the work past the live event: the code considered over ninety minutes is a starting position and the handout gives learners a structured way to keep revising as their AI use evolves. It also makes the workshop portable — someone who never attended can still work through it, helping the same artifact double as the participant guide.

In-house tools only.

I built the workshop on tools the university already had — PowerPoint, Google Docs, Google Forms, Trello, and Canva. A workshop built on common tools is replicable at resource-limited institutions and more importantly, easy for other facilitators to run.

Solution Architecture

Option A — Workshop

Presenter introduction – topic and agenda – learning objectives – what is AI → strengths/weaknesses → the right tools → scholarship considerations - ethical reflection → personal-code activity → closing prompt and wrap-up.

Option B — Symposium (with breakout sessions)

Panel introductions → AI background → "why a personal code" framing → breakout session 1 (20 min) → breakout session 2 (20 min) → reconvene and debrief → Q&A. Reflective handout and resource list distributed throughout.

The six breakout rooms

• Behind the Curtain: What is AI?

• AI's Impact on Learning

• AI and Its Impact on Resources and the Economy

• AI, Privacy, and Intellectual Property

• AI and Me

• AI Policy: Impacts on Teachers and Students

DESIGN

Learning Outcomes

By the end of the session, participants will be able to:

• Explain and summarize the basics behind how Gen AI works, its utility, and ways in which it can fail;

• Distinguish when AI supports their learning from when it shortcuts it;

• Evaluate the use of a specific AI tool against their own values and their community's interests; and

• Design an ethical personal and professional framework for appropriate AI use.

DEVELOP

Origination and Collaboration

The topic of this symposium is one I regularly advocate for as a recognized need and recruited cross-departmental input from the library, the writing center, the education department, and instructional technology. Those partners shaped what the sessions needed to cover; I chose to lead the instructional design element. Working across four units meant reconciling different priorities into a single coherent flow: I treated the departments as stakeholders whose input defined scope, then made the pedagogical and structural decisions myself. Convening that group was part of my needs assessment and a way to surface what a genuinely cross-disciplinary audience would need.

Constraints

Two constraints shaped my work. The first was production access, as I was limited to in-house Microsoft and Google tools, with no specialized or paid platforms. Keeping the product simple as a controllable slide deck allowed for sessions to move at the pace of the facilitator and each individual session’s discussion. The second was time. The application to present started the process, prompted the collaborative input, and required the full design be complete within two months to allow for final feedback and practice. From beginning to end the process for the symposium was 14 weeks; the workshops were developed in just over a month from concept to first presentation. Keeping the design process to a well-known tool using a PowerPoint deck helped streamline creation.

IMPLEMENT

Implementation and Iteration

The design was built to be flexible across deliveries – I facilitated this in two forms. With co-facilitators, my colleagues and I presented a symposium format, with self-selected breakout rooms and a take-away reflection. In this form it ran once as a special campus event with its own marketing piece, drawing a full-campus audience of over sixty faculty, staff, and students from all degree levels and programs. As a single facilitator, I regularly ran the workshop version. I scheduled three sessions a semester over five semesters, with attendance totals near 150.

Sustained use also allowed for design evolution. The original workshop centered on introductory materials and discussions: what AI is, how it works, where it fails, and the ethical issues behind scholarly use. As our community's baseline familiarity rose, I developed a second-generation version, swapping introductory content for guest speakers and special topics, while preserving the ethical-reasoning spine and the personal-code design.

Here I believe ADDIE became an evolving loop rather than a single effort. The special-topics generation was not planned at the outset but was a logical iteration from watching the design in use, evaluating the audience’s changing needs, and using that evaluation to start a new design cycle.

EVALUATE

Evaluation happened in formal and informal metrics. The formal metrics came from a scheduled email at the end of each workshop, inviting participants to complete a survey consisting of a 5-question Likert Scale covering content value, delivery, discussion comfort, learning value, and satisfaction / would recommend. As a voluntary workshop, these questions provided insight into how students felt about value versus time, what information they left with, and the perceived value to others. Approximately 47 attendees responded (1/3 of total) and ratings were reliably in the very satisfied to extremely satisfied (4 or 5 out of 5) area.

Informal metrics included email, Zoom text, and verbal comments at the end of both workshops and the symposium. Comments included value statements like “this was helpful”, “I’m much clearer on how I can use AI, thanks!”, and “you’ve given me a lot to think on, great discussion”, and more. While not detailed feedback, the positive responses were encouragement to continue offering this workshop

*screenshot of the survey

REFLECTION

Reflect and Iterate

A strength of this presentation was the collaborative work with multiple departments and disciplines. While some topics live inside a specific program area, Generative AI impacts everyone in the collegiate setting. Facilitating an open dialogue between faculty, staff, and students brought transparency to an emotionally charged topic.

It was also a place the workshop could veer off topic. Future iterations would be improved by clarifying target audiences and maintaining a clear focus on personal accountability.

The flexible and multi-option format of this presentation design was a strength of both its success and accessibility. I drew heavily on the literature to ensure it was easy to present and fully accessible to learners; some of my favorite sources included Tolulope Noah’s Designing and Facilitating Workshops with Intentionality: A Guide to Crafting Engaging Professional Learning Experiences in Higher Education; Cathy Moore’s Map It: The Hands-on Guide to Strategic Training Design; and Grant Wiggins and Jay McTighe’s Understanding by Design.

Future iterations would move the conversation forward both in developing specifically targeted topic areas - use of AI in healthcare, as an example. More formats include an interactive, asynchronous tutorial that allows learners to consider an ethical code at their own pace, and a short micro-learning recorded version as a review or introduction to deeper conversation.

Artifact Index

• Slide deck (panel / workshop) — [link]

• Breakout questions — [link]

• Reflective handout (fillable) / participant guide — [link]

• Facilitator guide — [link]

• Faculty survey report — [link]

• Code of conduct and resource list — [link]