Abstract:

The purpose of this study was to better understand the emerging needs for instructor digital competencies related to generative Artificial Intelligence (AI) in their teaching practices in higher education in California, United States. An anonymous survey was sent to all full-time faculty, lecturers, and instructional designers yielding 107 responses. Several survey questions were adapted from the original Technology Acceptance Model (TAM) survey that was initially developed by Davis (1989) and revised by Lewis (2019). The Technology Acceptance Model (TAM) is a theoretical framework that explains how users come to accept and use a new technology.

It identifies two key factors that influence an individual's decision to adopt and use a technology:
(1) Perceived Usefulness, which is the degree to which a person believes that using a particular technology will enhance their job performance or meet their needs,
(2) Perceived Ease of Use, which is the degree to which a person believes that using the technology will require minimal effort.

While AI tools have been introduced gradually over the past decade, the rapid adoption of generative AI tools like ChatGPT in the past couple of years has accelerated this shift, prompting both opportunities and challenges in adapting to AI-driven education. Despite some higher education institutions are providing free training about the best use of AI tools for teaching and learning, faculty members might be reluctant to participate in these trainings if they don't perceive the usefulness of these tools as part of their teaching or if they consider that it would be too difficult to learn how to use them effectively.

The Technology Acceptance Model is a robust and reliable framework for studying technology adoption across various fields, including education, healthcare, and business. Its importance lies in its simplicity and predictive power, which allow researchers to identify and analyze the factors influencing users' acceptance of new technologies. TAM provides a structured approach to understanding how perceptions of usefulness and ease of use drive attitudes, intentions, and eventual behavior in technology adoption. One of TAM's key strengths is its high level of reliability and validity, which has been demonstrated in numerous studies over the years. The model has been empirically tested and refined across diverse settings, consistently yielding insights into user behavior.

We used the TAM model to explore how faculty and instructional designers are inclined to use artificial intelligence in their work. Some of the questions included the level of familiarity with:
(1) AI Text Generator tools (e.g., ChatGPT, Bard/Gemini, Claude, CoPilot),
(2) Image Generator tools (e.g., Dall-E, MidJourney, Stable Diffusion) and
(3) other AI tools (e.g. Code generators, Music generators, Video generators, SCORM file generators.

The survey also included open-ended questions to examine issues such as barriers to using Generative AI Tools at work and the way they have used some of these tools.

Insights from this research could help institutions better understand the factors influencing AI adoption and develop strategies to support faculty in integrating AI into teaching, research, and administrative tasks.

Keywords:

Technology, Education, Artificial Intelligence, Technology Acceptance.