Conditions for Adoption: Building Faculty Capacity for AI Literacy
Jacob Holster, Ph.D. Assistant Teaching Professor of Music Education Coordinator, Center for Pedagogy in Arts and Design The Pennsylvania State University
Fotis Sotiropoulos, Executive Vice President and Provost of Penn State, has set a university-wide agenda centered on AI literacy for all, interdisciplinary innovation, and ethical leadership in generative AI. With this emerging shift, we may see familiar frictions across units: the need to layer new tools onto existing routines, uneven faculty confidence, added cognitive load for students, unclear use cases, and inconsistent training and support. Although policies and platforms will evolve quickly, course designs and advising practices may struggle to keep pace. In this landscape, the concept “AI literacy” risks meaning everything and nothing at once. To catalyze meaningful change, faculty first need a way to design for teaching with technology that centers coherence and learning value, followed by strategies to support AI adoption at scale. First, the Technological Pedagogical Content Knowledge (TPACK) framework provides a design approach for teaching with technology by aligning disciplinary aims (CK), pedagogical moves (PK), and purpose-built AI features (TK) so that activities produce visible evidence of learning that is aligned with best teaching practices and pertinent content. Second, the Unified Theory of Acceptance and Use of Technology (UTAUT) supplies a lens for adoption at scale by targeting the conditions that might move faculty from avoidance to intention to capable, confident, and ethical generative AI use in their teaching, research, and service responsibilities.
Integrating Pedagogy, Content, and Technology
The concept of pedagogical content knowledge (PCK) emphasizes that teachers must not only master disciplinary content but also understand how to transform it through instruction (Shulman, 1986). Mishra and Koehler (2006) extend this idea through the Technological Pedagogical Content Knowledge (TPACK) framework, arguing that effective teaching emerges when content knowledge (CK), pedagogical knowledge (PK), and technological knowledge (TK) intersect. When teaching pairs explicit learning sequences and techniques, content at an appropriate skill level, and hands-on exposure to digital tools, student interest and performance tend to improve (Mishra & Koehler, 2006).
Figure 1
The TPACK framework.
Understanding the Drivers of Tool Adoption
To understand why faculty or students embrace or resist new technologies, researchers have turned to the Unified Theory of Acceptance and Use of Technology (UTAUT). Venkatesh et al. (2003) identified four core determinants of behavioral intention: performance expectancy (the belief that using a system will lead to gains in job performance), effort expectancy (the degree of ease associated with using the system), social influence (the perception that important others think one should use the system), and facilitating conditions (the belief that organizational and technical infrastructure exists to support use). Moderating factors such as age, gender, experience, and voluntariness of use affect the strength of these relationships, and the model explains about 70% of the variance in behavioral intention (Venkatesh et al., 2003). A more recent model (UTAUT2) added hedonic motivation (pleasure derived from use), price value (benefit–cost trade-off), and habit (the extent to which behavior is automatic; Venkatesh et al., 2012).
Figure 2
The UTAUT framework.
Merging TPACK and UTAUT to Support Students
Bringing TPACK and UTAUT together offers a more nuanced understanding of technology adoption in education. As TPACK stresses that teachers must align content, pedagogy, and technology to enhance learning, UTAUT explains the psychological and contextual factors that influence whether teachers and students adopt those technologies. For a non-AI example, an instructor might know that a digital audio workstation (DAW) can make student thinking visible (TK) and design a scaffolded listening exercise (PK) to deepen aural skills (CK). However, if the DAW is perceived as cumbersome (low effort expectancy) or unnecessary (low performance expectancy), adoption will be limited.
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Similarly, social influence matters: when peers or mentors endorse a tool and provide support, individuals are more likely to persevere in developing fluency (Venkatesh et al., 2003). In the generative AI case, aligning AI interactions, pedagogical boundaries, and discipline-specific content to the learning task (TPACK) will scale only when faculty (and, by extension, students) perceive tangible gains, manageable effort, social acceptance, and supportive training infrastructures (UTAUT). For a practical example, in an unpublished study with elementary teachers (n = 84) conducted by Pei-Hsuan Lu (Music Ed PhD ’25), using a UTAUT+TPACK lens showed that strengthening facilitating conditions predicted competence, which supported expectancies for success and, in turn, increased intentions to adopt unfamiliar classroom technologies.
AI literacy, a prerequisite to AI fluency, sits at the intersection of these frameworks. When faculty understand the factors that drive their own adoption, they are better equipped to empathize with and support students’ experiences with AI. Conversely, faculty who lack TK may inadvertently select AI applications misaligned with content or pedagogy, or provide insufficient guidance — raising cognitive load and inequity (Mishra & Koehler, 2006). Building TPACK among faculty therefore requires awareness of adoption dynamics.
Figure 3
Elementary music teachers’ intentions to adopt new technologies.
Implications for Faculty Development and Institutional Support
Developing AI literacy calls for multi-layered professional development. First, AI literacy programs might focus on the performance expectancy of generative AI by demonstrating clear instructional benefits. Second, faculty training might lower effort expectancy by offering hands-on practice, quick-start guides, and ongoing support (Mishra & Koehler, 2006). Third, institutions can create an ecosystem of tools and social support through communities of practice, mentorship, and recognition of innovative teaching. Fourth, ensuring facilitating conditions like robust technical infrastructure, accessible platforms, and responsive help desks or expert networks supports a motivational pathway for habitual tool use. With these supports, built on the pedagogical content knowledge our faculty already bring, we may move past AI tool overwhelm and begin to see ourselves as guides for students’ AI literacy.
References
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.
Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
