This study examines the factors that may impact the adoption of generative artificial intelligence (Gen AI) tools for students’ assessment in tertiary education from the perspective of early-adopter instructors in the Middle East. It utilized a self-administered online survey and the Unified Theory of Acceptance and Use of Technology (UTAUT) model to collect data from 358 faculty members from different countries in the Middle East. The Smart PLS software 4 was used to analyze the data. The findings of this study revealed that educators developed new strategies to integrate Gen AI into assessment and used a systematic approach to develop assignments. Moreover, the study demonstrated the importance of developing institutional policies for the integration of Gen AI in education, as a driver factor influencing the use of Gen AI in assessments. Additionally, the research identified significant factors, namely performance expectancy, effort expectancy, social influences, and hedonic motivation, shaping educators’ behavioral intentions and actual use of Gen AI tools to assess students’ performance. The findings reveal both the potential advantages of Gen AI, namely enhanced student engagement and reduced instructor workloads, and challenges, including concerns over academic integrity and the possible negative impact on students’ writing and thinking skills. This study emphasizes the significance of targeted professional development and ethical criteria for the proper integration of Gen AI in educational assessment.

Background: Artificial intelligence (AI) is increasingly embedded in medical education, providing benefits in instructional design, content creation, and administrative efficiency. Tools like ChatGPT are reshaping training and teaching practices in digital health. However, concerns about faculty overreliance highlight risks to pedagogical autonomy, cognitive engagement, and ethics. Despite global interest, there is limited empirical research on AI dependency among medical educators, particularly in underrepresented regions like the Global South. Objective: This study focused on Palestine and aimed to (1) identify factors contributing to AI dependency among medical educators, (2) assess its impact on teaching autonomy, decision-making, and professional identity, and (3) propose strategies for sustainable and responsible AI integration in digital medical education. Methods: A qualitative research design was used, using semistructured interviews (n=22) and focus group discussions (n=24) involving 46 medical educators from nursing, pharmacy, medicine, optometry, and dental sciences. Thematic analysis, supported by NVivo (QSR International), was conducted on 15.5 hours of transcribed data. Participants varied in their frequency of AI use: 45.7% (21/46) used AI daily, 30.4% (14/46) weekly, and 15.2% (7/46) monthly. Results: In total, 5 major themes were identified as drivers of AI dependency: institutional workload (reported by >80% 46] of participants), low academic confidence (noted by 28/46, 60%), and perfectionism-related stress (46, 50%). The following 6 broad consequences of AI overreliance were identified: Skills Atrophy (reported by 89% [41/46]): educators reported reduced critical thinking, scientific writing, and decision-making abilities. Pedagogical erosion (35/46, 76%): decreased student interaction and reduced teaching innovation. Motivational decline (31/46, 67%): increased procrastination and reduced intrinsic motivation. Ethical risks (24/46, 52%): concerns about plagiarism and overuse of AI-generated content. Social fragmentation (22/46, 48%): diminished peer collaboration and mentorship. Creativity suppression (20/46, 43%): reliance on AI for content generation diluted instructional originality. Strategies reported by participants to address these issues included establishing boundaries for AI use (n=41), fostering hybrid intelligence (n=37), and integrating AI literacy into teaching practices (n=39). Conclusions: While AI tools can enhance digital health instruction, unchecked reliance risks eroding essential clinician competencies. This study identifies cognitive, pedagogical, and ethical consequences of AI overuse in medical education and highlights the need for AI literacy, professional development, and ethical frameworks to ensure responsible and balanced integration.

The acceptance and adoption of emerging technologies are crucial for their effective integration. This study examines the factors influencing educators’ acceptance of Generative AI (Gen AI) tools in higher education, guided by the UTAUT model. It also develops a structural model to explore the relationships between UTAUT constructs and behavioral intention (BI) to use Gen AI. Using a quantitative approach, the study collected data through a self‐administered online survey based on prior research findings. The survey gathered responses from 307 educators across various Arab countries who are early adopters of Gen AI in teaching. PLS‐SEM was used to analyze the data. Findings indicate that UTAUT constructs significantly and positively influence educators’ intention to use Gen AI. Additionally, the results highlight the complex role of gender and work experience, revealing diverse perspectives among educators from different countries. This study contributes to the literature by deepening the understanding of technology adoption factors. It also offers theoretical and practical implications for researchers and policymakers in designing strategies to integrate Gen AI into higher education in developing countries.