Navigating the AI-Enabled Education Landscape: A Multifaceted Approach to Providing Effective Professional Learning and Support for Educators

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Navigating the AI-enabled education landscape: A multifaceted approach to providing effective professional learning and support for educators 

Introduction

As generative artificial intelligence (GenAI) continues to reshape higher education, instructors need effective, accessible professional learning opportunities to enable them to make informed, intentional decisions about whether and how to integrate GenAI into their teaching. Educational developers are well positioned to meet this need. This article describes a multifaceted, scalable approach to pedagogical support for instructors across disciplines and institutional contexts. By combining synchronous and on-demand offerings — including research teams, workshops, learning communities, and open-access resources — we demonstrate a variety of ways that educational developers can build instructors' pedagogical skills and AI literacy. Through these efforts, instructors are better prepared to navigate the AI-enabled education landscape. We discuss the benefits and challenges of these approaches to professional learning, offering insights so that others tasked with professional development for educators can adapt these strategies to their own contexts.

While artificial intelligence (AI) technologies (e.g., those that make predictions based on identified patterns) have existed for over 50 years, the recent proliferation of generative AI (GenAI) tools such as ChatGPT has pushed educators to reconsider what it means to teach and learn, and to adapt their pedagogy accordingly. Unlike traditional or predictive AI (e.g., Netflix's recommendation engine), GenAI can create new content such as text, images, code, audio, and video using patterns learned from training data (Caballar, [5]). With powerful tools available with the potential to quickly complete students' coursework for them, it comes as no surprise that educators are concerned about learning loss. This concern has manifested in a variety of reactions to GenAI, from strict bans on usage and investments in AI detection software to allowing students to freely explore the potentials and pitfalls of using GenAI. Many educators feel the urgency to learn about GenAI and its implications for teaching and learning, and dozens of articles published in higher education-focused publications since late 2023 (e.g., D'Agostino, [8]; Latham, [21]; McMurtrie, [26]) indicate demand for relevant professional development.

As educational developers in higher education, we support instructors across the full spectrum of beliefs and practices related to GenAI. Some instructors approach these tools with deep skepticism or outright resistance, raising important philosophical, ethical, or pedagogical concerns. Others are enthusiastic early adopters, experimenting with ways to integrate GenAI into assignments, feedback processes, and even course and curricular outcomes. Most instructors fall somewhere in between — curious but cautious. Our role, then, is not to advocate for a single one-size-fits-all approach to GenAI in teaching and learning, but rather to support instructors in clarifying their own values and goals, exploring pedagogically sound uses of GenAI, and communicating transparently with students about what is expected in their courses.

This article describes a multifaceted, scalable educational development approach to providing effective professional learning and support for higher education instructors as they navigate the AI-enabled education landscape. A combination of synchronous and on-demand approaches — including research teams, workshops, learning communities, and open-access materials and resources — highlights a range of possibilities that balance workload and effectiveness, and center educator access and inclusion regardless of their perspectives on GenAI. Our efforts aim to help instructors enhance student learning through effective, evidence-based teaching practices.

Learning and GenAI

Common learning taxonomies, such as Bloom's Taxonomy and Fink's Taxonomy of Significant Learning, highlight a range of desired student learning outcomes, such as foundational knowledge, problem solving, and learning about oneself and others. These frameworks emphasize not only cognitive skills, but also affective and metacognitive dimensions of learning. GenAI has the potential to impact all kinds of learning, both positively and negatively, depending on how it is introduced and used in the classroom.

Instructors have legitimate and varied concerns about how GenAI may impact student engagement, academic integrity, and the development of foundational skills such as writing and critical thinking. There is emerging evidence that, when used intentionally and transparently, GenAI tools may support learning by providing scaffolding, feedback, or modeling (e.g., Guo et al., [12]; Lo et al., [23]; Mulyani et al., [27]; Yuan & Hu, [38]). At the same time, research on its short- and long-term impacts on learning is still in early stages. For example, a recent review and meta-analysis of the impact of ChatGPT on student learning found improved academic performance, enhanced affective motivation, strengthened higher-order thinking, and reduced mental effort required for learning (Deng et al., [10]). However, these results varied based on subject area, intervention setting, duration, and educational stage. As with any educational tool, GenAI is likely most beneficial when integrated intentionally and in alignment with specific learning goals. Otherwise, it stands to harm the very learning instructors seek to cultivate.

Consequently, AI literacy is crucial for instructors. Understanding GenAI's capabilities and limitations empowers educators to use these tools ethically and responsibly, and in service of learning. Ignoring GenAI's impact leaves instructors, and thus their students, ill-prepared for a world in which AI is embedded into personal, academic, and professional life. By providing effective professional development to instructors, we can move toward a future where GenAI is used to benefit, rather than circumvent learning.

Educational development and its role in supporting effective teaching and learning

Educational development encompasses a range of practices aimed at enhancing teaching effectiveness and student learning outcomes (Amundsen & Wilson, [1]; Wright et al., [36]). In a review of the published literature on educational development practices, researchers identified two primary focal areas of faculty development: first, the development of practical teaching strategies and conceptual understanding of teaching and learning; and second, the advancement of institutional and disciplinary approaches to pedagogy, including collaborative planning and inquiry-based professional learning (Amundsen & Wilson, [1]).

Effective educational development programs significantly enhance both individual teaching excellence and the overall institutional culture of teaching innovation. The positive impact of educational development is demonstrably linked to improved student learning (e.g., improved teaching practices in critical thinking directly correlate with enhanced critical thinking skills in student work; Condon et al., [6]) and various measures of student success (e.g., Hativa & Goodyear, [14]; Kuh et al., [20], [19]; Pascarella & Terenzini, [28]). Educational development that is responsive to instructors' needs (Behar-Horenstein et al., [4]; Cook & Meizlish, [7]; Sorcinelli et al., [29]) empowers instructors to adopt effective teaching strategies, leading to improved student learning and satisfaction (Kuh et al., [20]; Pascarella & Terenzini, [28]).

Effective educational development relies on several key principles. It is: (a) focused on student learning, (b) responsive to and empowering of educators, (c) offers opportunities for sustained and collaborative engagement, and (d) centered around inquiry. Research also suggests its effectiveness is significantly influenced by contextual/institutional factors (e.g., teaching load, research/publication expectations, mentoring responsibilities), particularly for contingent faculty who often lack institutional support (Hargreaves & Fullan, [13]; Hutchings et al., [16]; Kuh, [18]). Research on high-performing institutions reveals that a shared commitment to student success, supported by appropriate policies and resources, is crucial (Kinzie & Kuh, [17]). However, many current educational development programs prioritize functional skills training (e.g., technology integration, assessment) over a more inquiry-based approach to pedagogical excellence (Beach et al., [3]; Sorcinelli et al., [29]).

Support related to educational technology, in particular, benefits from a model that moves beyond tool training to holistically engage instructors. A systematic literature review by Lidolf and Pasco ([22]) found that effective educational technology professional development (ETPD) positions instructors in three interconnected roles: as learners, designers, and researchers. Instructors-as-learners engage in self-directed, immersive experiences that help them better empathize with students and embrace lifelong learning. Immersive learning experiences — where instructors take on the role of students — have been shown to deepen understanding of both the affordances and limitations of new technologies (Yilmaz et al., [37]). These kinds of experiences are not only pedagogically powerful but also foster increased empathy, adaptability, and reflective teaching practice. Instructors-as-designers, meanwhile, create technology-enhanced instructional materials and experiences; they design tasks that are not simply technical exercises, but are deeply tied to instructor identity and pedagogy. Instructors-as-researchers take on reflective and inquiry-based roles to analyze and improve their own teaching, often through action research or design-based research methodologies. This three-part approach to professional development aligns with findings on the importance of addressing instructor beliefs and values when promoting technology integration (e.g., Taimalu & Luik, [34]) and doing so within a supportive community of peers (Sullivan, [33]). It also supports sustainable and scalable educational change by positioning professional learning within the authentic contexts of teaching and inquiry, rather than as disconnected or one-off trainings.

While there are a multitude of approaches to educational development, we adapted Steinert's framework (Steinert et al., [32]) in developing professional learning for educators around GenAI. Steinert frames approaches along two continua: the Context for Learning, ranging from Individual to Group; and Faculty Development Approach, ranging from Informal to Formal. In addition to this framework, we relied on our team's collective educational development expertise and existing networks. The professional development activities described were developed as part of an existing network of educational developers, those who provide evidence-informed professional development to instructors, within the state of Virginia in the United States. They are not inclusive of all professional learning opportunities provided to higher education instructors at our institutions but rather highlight opportunities for engagement that are part of a coherent and balanced set of offerings.

Our approach to professional learning and support for higher education instructors

Our goal has been to provide effective, accessible professional learning that results in improved instructor confidence and competence with GenAI, enhances student learning, fosters innovation in teaching practices, and contributes to the knowledge base around teaching and learning with GenAI from both student and instructor perspectives. Each opportunity has been carefully developed to meet instructors where they are in their understanding of and beliefs about GenAI, and to be relevant to instructors across a range of educational contexts. Returning to Steinert's framework (Steinert et al., [31]), Figure 1 situates our approaches to professional learning to imagine individual and group experiences synchronously and on-demand. Table 1 presents a summary of the professional learning opportunities offered within this framework, along with their audience and intended outcomes and impact.

Graph: Figure 1. Our approaches to professional learning related to GenAI.

Table 1. Professional learning opportunities

Learning Opportunity	Intended Outcomes & Impact
Research Teams: Formed interdisciplinary, cross-institutional research teams to investigate teaching and learning with GenAI from student and instructor perspectives. Audience: Instructors, Researchers	Development of research skills Evidence to be used in the development of professional learning opportunities Conference presentations and articles
Workshops: Offering workshops throughout the geographic region, covering various topics. Audience: Instructors, Educational Developers	Cross-institutional collaboration Intentionally consider whether and how to integrate GenAI into courses and curricula
Professional Learning Communities (PLCs): Facilitating sustained engagement with a cohort of peers to explore specific GenAI topics and applications. Audience: Instructors	Opportunities for sustained engagement leading to deeper learning Development and implementation of course materials, learning activities, etc.
Open-Access Materials and Resources: Developing and sharing various on-demand, open-access resources. Audience: Researchers, Instructors, Educational Developers	Support for discussions of ethical and effective GenAI use in classrooms Adaptation/application of materials to participants' contexts Support for research teams to draft presentations and publications Expanded awareness of ethical and effective GenAI integration strategies Increased preparedness to leverage GenAI technologies in intentional ways Easy access to resources for instructors

Research teams

We began our professional learning efforts by initiating a Mega-Scholarship of Teaching and Learning (MegaSoTL) project — a systematic inquiry into teaching and learning across multiple institutions, which advances teaching in higher education by making findings public. Bringing together faculty, staff, postdoctoral research associates, and graduate students across the state, we formed eight cross-institutional, interdisciplinary research teams (with four to eight people per team) tasked with investigating student and instructor perspectives on and use of GenAI in higher education.

We chose to start our professional learning activities with these research teams because the GenAI landscape is ever-changing; carrying out our own research allows us to stay on the cutting-edge, attend to our context, and affords us close contact with those we wish to serve with our work. Additionally, experts in the field of educational development cite the importance of evidence-based practices, among other qualities, as necessary for meaningful and impactful educational development (Sorcinelli et al., [30]). Finally, participation in a research team focused on teaching and learning is itself a way for participants to develop their scholarly and academic skills (Lukes et al., [24]). As we mentored the research teams in conducting classroom-based inquiry projects, we expanded the capabilities of the team members in conducting this form of educational research.

These rigorous investigations yielded rich qualitative and quantitative insights across diverse educational contexts. The research, valuable in its own right, serves as the foundation for the professional learning and support we provide. As a result of our research, we are supporting instructors in:

Exploring how students can use GenAI for personalized, constructive writing feedback

Identifying ways students with disabilities are (not) using GenAI to support their learning

Designing in-class activities to introduce students to GenAI

Applying a taxonomy to evaluate the relative "AI-proofness" of a given assignment

Interrogating instructors' perceptions of the impact of GenAI on the teaching of writing

Measuring student learning when assignments are completed in collaboration with GenAI

Fostering AI literacy for students and themselves

Promoting ethical and responsible GenAI use in teaching and learning

Preparing students for a GenAI-enabled workforce

We recognize that not every person or team that provides professional learning opportunities will be able to use their own research as a starting place. When possible, it has great value; if it is not, then identifying trusted sources and resources for research related to teaching with AI provides a solid foundation for future professional learning opportunities.

Workshops

Workshops are among the most common approaches to providing professional learning opportunities for instructors. They afford participants the opportunity to interactively engage with content, the facilitator(s), and one another, often with the ability to apply what they learn to their own context through a hands-on activity or deliverable. In developing workshops to intentionally engage educators across institutions, we hope that participants will also find opportunities for cross-institutional collaboration and learn how different colleagues and institutions are navigating teaching in a world with GenAI.

At their core, each workshop we offer is intended to support instructors in intentionally considering whether and how to integrate AI into courses and curricula. We focus the workshops on concrete examples of ways to change classroom behavior, as small changes to instructor behaviors (e.g., developing an AI policy and communicating it to students) are easier and more realistic in the short-term and with one-off engagement. We also recognize that every educator who enters our programming comes with their own unique combination of skills, knowledge, experiences, and teaching contexts; a one-size-fits-all approach is almost guaranteed to be unsuccessful. Thus, our intent is to provide educators with the information they need to make informed decisions about what aligns with their own teaching persona and context. At the time of writing this article, we have three workshops planned, informed by the work of our research teams and current trends and interests in teaching with GenAI.

In Workshop #1, Empowering Educators: Communicating about Ethical AI Use in Your Courses, participants explore the ethical dimensions of AI in education, including redefining academic integrity and creating policies that promote an ethical AI culture. Through engaging discussions, case studies, and individual work, they develop a coherent AI policy tailored to their courses and gain practical strategies for guiding their students in responsible AI use. This workshop has been designed to be modular, so components can be added and removed to suit a variety of formats, from a 3-hour pre-conference workshop to a 90-minute Zoom webinar. By leveraging their expertise in course design, transparent assignment design, and inclusive teaching, educational developers can support instructors in fostering trust and transparency with students related to GenAI.

Workshop #2, Curriculum Design for the AI Era, is designed to provide instructors with strategies to thoughtfully design class activities and assignments that respond to the opportunities and challenges posed by GenAI. Participants will explore how GenAI is reshaping learning environments, assessment practices, student support needs, and disciplinary approaches to teaching and learning. This workshop leans heavily into the findings of our student-focused research teams. Their interdisciplinary, classroom-based research enables us to showcase rich examples of classroom practices with demonstrable positive impacts on student learning. By partnering with instructors at their institution innovating with GenAI, educational developers can provide concrete examples and inspiration for instructors.

Workshop #3, Preparing Students for an AI-Powered Workplace, is designed to help instructors and staff acquaint students with using GenAI in professional settings while prioritizing uniquely human strengths so that students graduate with the mind-sets and capabilities to thrive in workplaces alongside AI systems. We invited industry experts from around the state to partner in developing and facilitating the workshop, ensuring the content is aligned with current industry needs. Participants will be able to define valued skills, ethical behaviors, and productive human-AI collaboration as preparation for labor market needs. By partnering with local technical schools, community colleges, and community industries, educational developers can help to highlight translatable career-ready skills.

Professional learning communities (PLCs)

Professional learning communities (PLCs) gather small groups of educators, ideally fewer than 10 (Hipp & Huffman, [15]) for sustained engagement in a collaborative setting. Opportunities for collaboration with colleagues, within and across disciplines, in sustained efforts focused on improving teaching is an important component of effective educational development. Five essential characteristics of PLCs include the development of shared values and norms, clear and consistent focus on student learning, reflective dialogue, de-privatizing practice to make teaching public, and focusing on collaboration (Newmann et al., 1996, as cited in Vescio et al., [35]). Strong PLCs include "shared intellectual purpose and a sense of collective responsibility for student learning" (Darling-Hammond et al., [9], p. 17).

Differentiated professional development can be desirable; one might imagine grouping together educators with similar perspectives on or experiences with GenAI, or who are teaching in similar disciplines and contexts. Our learning communities have been developed to facilitate cross-disciplinary exchanges across a wealth of experiences with GenAI. In this way, those less experienced can benefit from the expertise of others in the group, and those with expertise get to engage with new perspectives and further their own learning by teaching others.

When done well, PLCs positively impact teaching practice and student achievement (Vescio et al., [35]) and provide space for educators to support each other in engagement with challenging topics related to teaching and student learning (Bayraktar et al., [2]). In learning communities, educators can reflect on their teaching, learn new strategies, experiment and receive feedback, all in a supportive peer group.

Our first PLC, Fostering AI Literacy for Students and Ourselves, provides participants with access to the Open Educational Resource (OER) we created (described further under Open-Access Materials and Resources) entitled Fostering AI Literacy: A Guide for Educators in Higher Education. Throughout the semester, instructors collaboratively develop their AI literacy, explore practical strategies for integrating AI literacy into course design, and identify approaches to foster equitable and inclusive learning environments where students gain both the skills and critical thinking needed to use AI effectively, responsibly, and ethically.

Open-access materials and resources

While there are many benefits of in-person or online synchronous forms of professional development, there are several notable challenges when offering opportunities face-to-face. For example, not all instructors are local to their institution, and even for those that are, it can be challenging to find time to attend events. Providing curated online resources allows instructors to access materials asynchronously at times and locations convenient for them. Attending to access was particularly important for this project given that it was designed to meet the needs of instructors across an entire state.

The proliferation of resources related to GenAI in teaching and learning is a double-edged sword. Resources may be easier to find, but at the same time, the volume may make it difficult for instructors to identify the highest quality and most relevant ones. Studies of instructor perceptions of online resources indicate that trusting the source of the material is an important factor in perceived utility (Maloney et al., [25]). In developing resources based on our research and disseminating them through our Centers for Teaching and Learning, our resources carry weight as trustworthy.

We are in the process of developing resources such as: (a) a set of classroom activities for introducing ethical/appropriate GenAI use to students, (b) an open educational resource (OER) to develop instructors' AI literacy for classroom teaching, (c) several "workshops-in-a-box" for educational developers to use with instructors, (d) an annotated bibliography, which summarizes relevant research articles, blogs, interviews, etc., on research and applications of GenAI in higher education, and (e) a curated list of externally developed materials, including videos, website links, and sample instructional materials.

For example, (b) is an OER we created entitled Fostering AI Literacy: A Guide for Educators in Higher Education. The OER outlines essential AI competencies for instructors and students and features interactive content, specific classroom examples, reflection questions, and hands-on activities. It also has community discussion features. And (d) is a careful consolidation of current research and discourse on AI applications in pedagogy. The annotations distill the key insights from each source, allowing readers to quickly understand its relevance without reading the full text. This saves researchers time while ensuring they do not miss critical perspectives that can inform new projects. Equally as important, the bibliography promotes awareness of AI tools among instructors. Educational developers, particularly those with expertise in the scholarship of teaching and learning (SoTL), are well suited to curating educator-friendly summaries of research articles and other resources, highlighting key findings with a focus on practical implications for classroom teaching.

Overall, the breadth of sources included makes them uniquely valuable both for researchers seeking an efficient way to stay cutting-edge and for instructors seeking an accessible entry point to engage with an emergent technology.

Recommendations for enacting professional learning related to GenAI

Considering the diverse needs, interests, and concerns of our audience, the following recommendations are grounded in both the design principles of our project and the real-world constraints faced by instructors and educational developers. As others consider adopting some of these approaches or adapting materials for use within their own contexts, a structured and thoughtful implementation strategy is crucial. Success hinges not only on selecting appropriate tools and techniques, but also on effective integration and ongoing evaluation. Systematic and coherent educational development initiatives are more likely to result not just in individual but in institutional change than other, less systematic approaches (Steinert et al., [31]).

Intentionally address diverse learner needs

Balancing individual and group synchronous and on-demand engagement was purposeful, to offer instructors multiple means and modes of learning. Planning for both synchronous and on-demand delivery of key resources helps us to meet our desired outcomes (as outlined in Table 1) with a diverse audience. Importantly, we had in mind audiences that traditionally have more difficulty participating in long-term, high-commitment professional development (e.g., part-time faculty, graduate teaching assistants, staff who engage in various instructional roles) due to workload, funding, and other barriers to participation.

Our project design also incorporated team members from various disciplines (e.g., instructors in STEM, humanities, and professional schools; staff in libraries and student services) and academic levels (e.g., undergraduate and graduate). This team diversity supported efforts to make resources adaptable and useful for instructors across disciplines and contexts.

Leverage existing resources and foster collaboration

Existing resources, such as those provided in this article and those available through professional organizations and online repositories, provide valuable starting points. Instructors and educational developers should begin by thoroughly reviewing these resources to identify approaches and materials that align with their teaching/facilitation goals and learner needs. Furthermore, they should actively engage in collaborative discussions with colleagues. Sharing experiences, challenges, and successful strategies can significantly accelerate the adoption process and prevent unnecessary duplication of effort. Consider establishing a peer mentorship program or a dedicated forum for sharing best practices within a department or institution.

Develop a phased implementation plan

The exigence of GenAI proliferation should not force instructors into a complete overhaul of teaching practices or overwhelm learners with too many new tools and approaches. Instead, a phased approach of piloting new tools or techniques in a single course or with a smaller group of students allows for iterative refinement based on feedback and observation. Gradually expand the implementation to other courses and student populations as implementers gain confidence and experience, documenting successes, challenges, and adjustments made.

Measure impact and adapt accordingly

It is crucial to establish clear metrics for evaluating the success of adopted tools and activities, and these metrics should align with learning objectives and may include student performance data (e.g., grades, assessment scores), student feedback (e.g., surveys, focus groups), and observations of student engagement.

Provide ongoing support and training

Successful adoption often depends on adequate support and training for both instructors and students. The materials created by the authors and the larger project team are designed to support individuals as well as groups of instructors in thinking about the implications of GenAI in the classrooms. It will take more than the occasional workshop to empower instructors to make intentional, informed decisions about GenAI. While the tools are rapidly changing, ongoing and deep learning about how to evaluate the use of educational technology and how to consider applications relevant to diverse contexts is ever vital.

Conclusion

As GenAI technologies continue to evolve, so too must approaches to teaching, learning, and faculty development. This project builds on extensive expertise in educational development and a network of teaching and learning innovators already catalyzing change across institutions. Through interdisciplinary research; workshops; PLCs; and open-access resources and materials, this project showcases a variety of ways to enhance professional learning for higher education instructors. The cross-institutional partnerships at its core enhance our ability to offer responsive, research-informed programming that meets instructors where they are. As educational developers, we must invest in sustained, critically engaged professional learning that not only builds AI literacy but also empowers instructors to navigate this complex landscape.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional resources for classroom use

1. Bowen, J. A., & Watson, C. E. (2024). Teaching with AI: A practical guide to a new era of human learning. Johns Hopkins University Press.

This book provides a clear and accessible overview of the ways in which AI is transforming the ways we think, learn, teach, and work. Educators will find not only valuable context but also practical examples of prompts to facilitate an interactive reading and learning experience.

2. Teaching Hub (n.d.). Generative AI in teaching and learning. Retrieved from https://teaching.virginia.edu/galleries/generative-ai

Teaching Hub is a free website designed to crowd-source pedagogical resources. Within Teaching Hub's generative AI gallery, each collection of resources provides both informational and practical resources for teaching and learning with generative AI. The resources are ever evolving, making it a valuable website to bookmark, use, and share with colleagues.

3. Gravett, E. O., and Broscheid, A ([11]). Models and genres of faculty development. In B. Berkey, C. Meixner, P. M. Green, & E. A. Eddins (Eds.), Reconceptualizing faculty development in servicelearning/community engagement (pp. 85–106). Routledge.

This chapter lists and describes the common types of faculty development activities that educational developers employ. While the authors discuss these in the context of service-learning, the descriptions of the genres apply across a range of educational development topics. The authors suggest all planned activities begin with determining the outcomes of the professional development and selecting a model that best meets those outcomes. Those who provide professional development can use this guide to plan and implement programs related to AI that suit the intended outcomes for their audience.

By Breana Bayraktar; Dayna Henry and Jessica Taggart

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