A Four-Stage Model for Faculty Development in GenAI: A New Evidence-Based Playbook from OLC

How do faculty developers quickly and effectively prepare faculty to integrate GenAI into their curriculum in a way that enhances teaching and learning? A four-stage developmental model in a new resource from the Online Learning Consortium (OLC) outlines a potential way for Centers for Teaching and Learning to organize their efforts.

Faculty Development and GenAI Playbook: Evidence-based Best Practices offers guidance on the scope and direction of generative AI policies and discussions in today’s higher education landscape. It guides faculty development professionals in colleges, universities, and other higher education organizations to help develop and implement GenAI professional development opportunities.

The insights in the playbook are based on a mixed-methods study conducted by OLC to investigate how Centers for Teaching and Learning (CTLs) are supporting faculty and instructors in integrating GenAI into their teaching practices, with a focus on training programs, effectiveness measures, and barriers encountered in this process. The playbook was developed in partnership with Every Learner Everywhere®, and the co-authors include Carrie Lewis Miller, PhD, Dylan Barth, PhD, and Josh Herron, PhD from OLC, Kristen Gay, PhD from EDUCAUSE, and Finn Scherer from Minnesota State University, Mankato.

The heart of the playbook is a model for faculty development in GenAI programming the OLC research team identified based on the results of the study. The model has four “stages”: Awareness and Foundations, Engagement and Skill Building, and Integration and Institutionalization, with a critical stage dedicated to Iteration as needs evolve. The playbook presents best practices, examples, important considerations, and other helpful insights about each stage.

Below we have excerpted and lightly edited sections of the playbook that define each stage of faculty development on GenAI. Be sure to browse the complete playbook to see actionable strategies, examples of what other institutions are doing, and the supporting research, as well as more comprehensive discussions of the material below.

Awareness and Foundations

The focus of this stage is to build comfort, trust, and basic understanding of GenAI through a variety of best practices.

At this foundational stage, simply offering sessions often suffices to spark participation. However, many faculty — especially skeptics and those unfamiliar with AI — remain disengaged, even with extensive outreach or incentives. Their concerns around ethics, bias, and academic integrity are valid and must be acknowledged as part of any responsible introduction to GenAI.

Survey results show that 92.86 percent of CTLs offer facilitated workshops and 83.33 percent provide one-on-one consultations to support faculty exploration at this stage. In open responses, many CTLs reported early success with “AI Playgrounds,” listening sessions, and book groups to demystify GenAI and reduce resistance.

Framing sessions around topics such as “AI for your workflow” — for example, using GenAI to draft emails, create syllabi, plan lessons, or generate feedback — offers immediate relevance and lowers the barrier to entry. Live demonstrations of these tasks help faculty see the practical value of GenAI in real time. Developing discipline-specific examples and partnering with faculty to co-design resources will build contextual trust and usability.

Despite growing interest in AI-related programming, CTLs face persistent challenges in sustaining engagement, especially as faculty navigate competing demands, burnout, and limited resources. Many still view GenAI as an added burden to their already extensive faculty responsibilities. That’s why the Awareness and Foundations stage must focus on building trust, reducing fear, and fostering curiosity — not pushing adoption.

By grounding GenAI training in familiarity, relevance, and reflection, CTLs can lay a strong foundation for future exploration. The goal at this stage is not mastery, but comfort, confidence, and curiosity — the essential building blocks for responsible and informed experimentation.

Engagement and Skill Building

The goals of this stage are to deepen learning, build communities, and foster early adoption of GenAI tools. This stage provides faculty with hands-on opportunities to apply GenAI tools in discipline-specific contexts, supported by peer learning and responsive instructional design. It’s where curiosity evolves into confidence, thoughtful experimentation begins to take root, and faculty begin integrating GenAI into their teaching in meaningful, discipline-relevant ways.

Engagement increases when sessions are tailored to real instructional needs — such as using GenAI for simulation design, scaffolding student writing, or redesigning assessments. Faculty are more likely to adopt tools when they can clearly see how GenAI enhances their existing practices rather than disrupts them.

Department-specific case studies and discipline-aligned examples are especially effective at this stage. Co-designing tools and teaching materials with faculty from each college ensures that solutions are not only relevant but also trusted and sustainable.

At the Engagement and Skill Building stage, peer leadership and cross-disciplinary collaboration become essential. However, one-on-one consultations (rated as “very effective” by nearly 49 percent of respondents) and discipline-specific workshops were noted as the most impactful professional learning opportunities by study participants.

Identifying and supporting GenAI “faculty fellows” or champions within each college helps build momentum and credibility. These individuals can lead workshops, mentor colleagues, and share their experiences at mini-conferences or showcase events.

To sustain engagement, CTLs should also create ongoing communities of practice where faculty can share challenges, successes, and evolving strategies. These spaces allow for deeper reflection on pedagogical shifts, student outcomes, and ethical considerations.

Integration and Institutionalization

The goals of this stage are to support long-term adoption, innovation, and infrastructure around GenAI once faculty have started to use it meaningfully in their teaching. Integration and Institutionalization is about aligning GenAI practices with long-term goals, updating policies, embedding GenAI into professional learning ecosystems, and scaling what works. This stage emphasizes coordination, recognition, and agility, ensuring that GenAI use is both effective and ethically grounded across the institution.

Policy development remains a challenge, particularly in large or decentralized institutions, as they struggle to keep pace with rapidly evolving technologies. A total of 69 percent of survey respondents indicated that their CTLs were actively involved in shaping institutional GenAI policy, often through faculty advisory committees or academic affairs collaborations.

Concerns persist about students’ overreliance on AI and its potential to hinder skill development, underscoring the importance of fostering AI literacy as a core competency for both faculty and students. Institutions are embedding GenAI into existing professional development programming — integrating examples into workshops, book chats, and mini-conferences — and using badges or certifications to recognize faculty engagement.

Many campuses are establishing central AI leadership roles or committees to coordinate efforts and promote cross-departmental collaboration. Staff supporting GenAI initiatives rely on hands-on experimentation, external learning, and internal partnerships to stay informed and build effective training programs.

Looking ahead, institutions are preparing for future trends by maintaining a flexible suite of professional development offerings, anticipating shifts in curriculum design, and fostering a culture of curiosity and critical engagement. This includes encouraging open dialogue about the benefits and risks of GenAI, supporting discipline-specific applications, and promoting thoughtful, responsible integration of AI into higher education.

Frequent Iteration

This stage is meant to flow throughout the process rather than be a “fourth” stage in a linear progression. Revisit your programming throughout each of the other three stages and make adjustments as necessary.

GenAI technologies and their implications are evolving rapidly. As a result, even the most well-integrated programs must remain responsive and adaptive. A continuous cycle of reflection, feedback, and renewal ensures faculty development efforts stay relevant, data informed, and aligned with emerging needs. Institutions that embed iteration into their GenAI strategies are better positioned to navigate uncertainty, embrace innovation, and sustain long-term impact.