Artificial Intelligence (AI) is rapidly transforming education, offering novel opportunities to enhance instructional design, personalize learning experiences, and foster engagement. From automating routine administrative tasks to providing adaptive, real-time feedback, AI-powered tools are emerging as valuable assets for educators and learners alike. However, the successful and sustainable integration of such tools depends not only on their technical capabilities, but also on their usability and acceptance by end users. As educational institutions increasingly adopt AI technologies, gaining insight into how both teachers and students perceive and engage with these tools is crucial to realizing their full potential.

This study investigates user experiences with two AI-driven educational applications developed within the framework of a European project:
(1) a digital platform designed to support teachers with lesson planning, resource management, and assessment development, and
(2) a conversational assistant designed to help students organize and manage their self-study routines. Using the Technology Acceptance Model (TAM), the research explores user perceptions across four key dimensions: Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitudes Toward Use (ATU), and Self-Efficacy (SE).

A total of 117 participants, 32 educators and 85 students, engaged in structured workshops conducted across four European countries. Each workshop followed a standardized format, beginning with guided demonstrations of the applications, followed by hands-on exploration tailored to participants' educational contexts. Following these sessions, participants completed a TAM-based questionnaire including 14 Likert scale statements adapted from validated instruments. Descriptive and inferential statistical analyses were conducted to examine acceptance levels and group differences.

Findings reveal a generally high level of acceptance among both groups, with mean scores exceeding 3.5 (on a 5-point scale) across all TAM dimensions. SE received the highest ratings, indicating strong user confidence in navigating the applications independently. However, PU scored lowest among the four constructs, suggesting that while the applications are easy to use, their alignment with real-world educational tasks requires further refinement. Comparative analysis shows no significant differences between teachers and students for PEOU and PU. However, teachers demonstrated significantly more positive attitudes and higher self-efficacy than students.

These results highlight the importance of user-centered design and pedagogical relevance in the development of educational AI tools. They also underscore the need for differentiated implementation strategies to bridge confidence gaps and foster equitable engagement. By addressing user expectations and enhancing support mechanisms, institutions can facilitate more effective integration of AI into educational practice.

This study contributes to the growing body of research on AI in education by providing empirical insights into the experiences of teachers and students. It calls for future research to explore long-term adoption patterns, ethical considerations, and the development of inclusive strategies that reflect the diverse needs of educational stakeholders. Aligning technological innovation with user trust, training, and pedagogical goals is essential to creating impactful and learner-centered AI-enhanced environments.

Keywords: Artificial Intelligence, Technology Acceptance, Education.