Faculty AI Perceptions Study

Project Overview

The Challenge

In Fall 2024, artificial intelligence emerged as a pressing instructional challenge, yet institutional understanding remained fragmented. While the CTL had introduced an AI literacy micro-credential, faculty still lacked guidance on developing clear, course-level AI policies, creating risks for inconsistent student expectations and assessment integrity. Faculty perspectives ranged from resistance to integration, with many feeling uncertain or overwhelmed. Departmental differences and limited insight into adjunct practices further complicated the landscape. Without a shared framework or clear data on faculty needs, the CTL lacked the direction needed to effectively guide training and policy development. Expand for more details
  • The CTL needed needed to determine what training to offer, how to prioritize it, and how to scale support but lacked sufficient data to make informed, strategic decisions
  • There was a need for behavior-based insight to inform training decision making that reached past faculty opinions, and into how they were actually using (or not using) AI in practice
  • AI adoption by students accelerated quickly in Fall 2024, outpacing institutional structures, guidance, and faculty preparedness
  • Prior CTL had implemented an AI literacy micro-credential but had not developed a training for devising course policies and adapting course assessments
  • Faculty lacked best practicce guidance on how to create clear, enforceable AI policies which resulted in inconsistent messaging to students about acceptable AI use
  • Faculty lacked a bird’s eye view regarding colleague practices to help inform their own practices with an evolving innovation, many expressed curiosity but lack access to practical strategies that were working for peers in other discipline areas
  • Gaps in existing policies: some faculty assumed existing plagiarism policies were sufficient; however, these policies did not fully account for the nuances of AI-assisted work
  • Adjunct faculty unknowns: adjunct instructors had full autonomy over course design, therefore there was limited insight into their practices, needs, or level of support required risking further inconsistency
  • The CTL needed needed to determine what training to offer, how to prioritize it, and how to scale support but lacked sufficient data to make informed, strategic decisions

The Design Approach

I developed and implemented a mixed-methods needs assessment to evaluate faculty Levels of Use (LoU) of AI in teaching. After securing IRB approval, I created a conditional interview protocol for full-time faculty and a branching survey for adjunct faculty, and trained a small team using a dual-rater approach to ensure consistency. Data collection in Spring 2025 resulted in 82% full-time faculty participation and a 59% adjunct response rate, producing a strong dataset to inform training and policy development. Expand for more details
  • Research initiative aligned with a request from the Board of Trustees for insight into institutional AI trends
  • Sought and obtained Institutional Review Board (IRB) approval because findings would be shared with faculty and the Board of Trustees and become part of the public record
  • Developed a structured Levels of Use (LoU) interview protocol in Fall 2024 to assess full-time faculty comfort and practices related to AI in their courses
  • Designed a branching survey for adjunct faculty to account for scale and limited access to interviews
  • Intentionally used flexible, open-ended wording to capture a wide range of perspectives on a rapidly evolving topic
  • Built the interview protocol with conditional questioning pathways to adapt based on participant responses
  • Structured interviews to identify each faculty member’s Level of Use (LoU) by the conclusion of the conversation
  • Prepared and trained interview team consisting of the CTL Director, Instructional Designer, and Instructional Technologist using norming protocols
  • Achieved an 82% participation rate among full-time faculty through interviews
  • Achieved a 59% response rate from adjunct faculty through surveys
  • Generated a comprehensive, behavior-based dataset that moved beyond anecdotal feedback to inform training and policy decisions

Outcomes and Impact

The assessment revealed clear patterns in how faculty were engaging with AI, allowing the CTL to move from assumptions to targeted action. By identifying where faculty were in their adoption, we were able to design a focused micro-credential on AI and authentic assessment that directly addressed their needs and supported more consistent, effective course practices. Expand for more details
  • Expanded professional development and faculty engagement: Led to the creation of the “AI and Authentic Assessment” micro-credential and increased faculty demand for one-on-one coaching, positioning the CTL as a key support resource
  • Established and communicated clear faculty adoption patterns: Identified that full-time faculty were concentrated in mechanical and routine use stages with a split between non-use and refinement, while adjunct faculty were largely in early and mechanical stages, indicating functional engagement but limited instructional transformation
  • Identified targeted support needs: Revealed the need for stronger onboarding and foundational support for adjunct faculty and deeper, practice-focused support for full-time faculty to move beyond basic implementation
  • Communicated institutional inconsistency and gaps: Highlighted wide variation in AI use across departments, lack of a shared framework, and uneven student experiences due to independent faculty decision-making
  • Clarified policy and academic integrity challenges: Identified absence of standardized approaches to AI use and detection, low confidence in AI checkers, equity concerns, and inconsistent faculty responses to suspected AI use
  • Documented shifts in instructional practice: Captured movement toward process-based assessment, including requiring students to show their work, and early integration of AI as a teaching and learning tool
  • Informed institutional strategy and policy development: Shared findings with faculty and the Board of Trustees, directly contributing to institutional discussions and decisions around AI-related policy and practice

Project Walkthrough


Click here to view the accessible version of this interactive content

Reflection and Transferability

This project highlighted the value of combining surveys and interviews to capture both broad input and deeper insight into faculty practice. While surveys effectively reached adjunct faculty, the Levels of Use interviews revealed more nuanced patterns, including wide variation in confidence, discipline-specific approaches, and the role of strategies like proctoring. I was also encouraged by faculty experimentation, such as using AI as a self-tutoring tool. If repeated, I would plan earlier for broader dissemination and develop more targeted support for adjunct faculty. This approach is highly transferable to other emerging topics, providing a structured way to identify patterns and design responsive, data-informed professional development.

Relevant Testimonials

Click on card title to expand for details.

Artificial Intelligence Professional Development Alan Wendlandt Instructional Designer, Western Wyoming Community College

At Western Wyoming Community College, we intentionally designed our AI professional development to meet faculty where they were, recognizing that AI disruption affects instructors at very different levels of comfort and readiness. Our Center for Teaching and Learning team, led by Rhonda, used Levels of Use surveys to better understand how faculty were currently engaging with AI and where their concerns and needs existed. This data directly informed how we structured our sessions, allowing us to address issues that were specific and relevant to Western rather than relying on generic AI guidance. From my perspective as the instructional designer, this approach was successful because it not only shaped more targeted professional development but also gave us valuable background knowledge to differentiate our one-on-one support, helping faculty move forward in ways that aligned with their individual teaching contexts and goals.

Select the card title to collapse.

Fulltime Faculty Michelle Zuppa Department Chair English & Humanities Associate Professor of English Composition, Western Wyoming Community College

As English faculty at Western Wyoming Community College, I’ve had the pleasure of working with Rhonda Gamble, Director of our Center for Teaching and Learning, for the past three years. In this time, Rhonda has impressed me with her knowledge, problem-solving abilities, and caring nature. Instructors could count on regular training sessions from Rhonda and her team on pertinent topics ranging from engagement, online presence, mapping learning outcomes, and coping with artificial intelligence. On this last topic, Rhonda offered resources as soon as it was clear that this topic was a major challenge, providing syllabus language to suit different instructor preferences, strategies for using AI detectors, and guidance for incorporating ethical AI use in the classroom and to make our own workloads more manageable. Rhonda is also helpful with one-on-one concerns. She and I have discussed discussion post strategies, different AI use assignments, handling issues with students, accommodations, and even strategies for norming and consistently applying rubric standards on writing assignments. Rhonda’s information is always research-based, collaborative, and up to date. She is also always kind, caring, and aware of faculty concerns. I recommend her very highly and know that she will make significant contributions to any organization she serves.

Select the card title to collapse.

Fulltime Faculty Dr. Joshua Holmes Department Chair Science & Outdoor Recreation, Associate Professor of Microbiology Western Wyoming Community College

I am writing to offer my higher recommendation for Dr. Gamble, whom I have had the pleasure of working with for the past three years in her role with the Center for Teaching at our college. During this time, she has been an exceptional resource, collaborator, and colleague whose impact on my teaching and course design has been both significant and lasting.

Dr. Gamble has played a critical role in helping me develop and strengthen my curriculum through thoughtful, effective integration of instructional technology. She guided me in incorporating tools such as Kahoot to enhance student engagement, and she had consistently provided expert support in improving both the visual design and functional organization of my Canvas courses. As a result, my courses are more accessible, intuitive, and effective for students.

One of Dr. Gamble’s greatest strengths is her ability to navigate emerging challenges and opportunities in higher education, particularly in the rapidly evolving area of artificial intelligence. She has been instrumental in helping faculty thoughtfully understand, adapt to, and responsibly incorporate AI-related consideration into course design and instruction. Her guidance in this area has been both practical and forward-thinking, and it reflects her deep commitment to teaching excellence and student success.

Beyond her technical expertise, Dr. Gamble is an outstanding colleague. She serves as a trusted sounding board for ideas related to course design, pedagogy, and innovation, always offering insightful, constructive feedback. She approaches her work and her interactions with warmth, professional, and genuine care, marking her a pleasure to collaborate with. Her ability to combine expertise with approachability sets her apart and strengthen every team she is a part of.

I give Dr. Gamble my strongest possible recommendation. She would be a tremendous asset in any role focused on faculty development, instructional design, or teaching and learning support, and I am confident she will continue to make meaningful contributions wherever she goes.

Select the card title to collapse.