<![CDATA[This study aims to describe the integration of the AI-Edu Assistant within an appreciation-based deep learning strategy to enhance the motivation and learning outcomes of vocational high school students. Alongside the rapid development of artificial intelligence in education, this research explores how AI can support the provision of personalized feedback, digital appreciation, and adaptive learning recommendations as components of an innovative instructional design aligned with 21st-century educational needs. The study employs a qualitative descriptive approach supported by quantitative data. Data were collected through classroom observations, teacher and student interviews, documentation, and pre- and post-learning assessments. The instructional process implemented four phases of deep learning-exploration, elaboration, application, and reflection which were combined with teacher-provided appreciation and automated appreciation messages generated by the AI-Edu Assistant. Feedback logs and AI-generated recommendations were also analyzed as supporting data. The findings indicate that the integration of the AI-Edu Assistant successfully enhanced students’ learning motivation, as evidenced by increased participation, persistence, and willingness to ask questions and express opinions. Students responded positively to the instant appreciation and adaptive feedback provided by the AI system. Learning outcomes increased from an average score of 68 (pretest) to 80 (posttest), demonstrating a significant academic impact. This study reveals that combining AI-based feedback with an appreciation-based deep learning strategy can create a more personalized, interactive, and motivating learning environment. The findings offer a practical model for integrating AI into pedagogical strategies and contribute new insights into the role of AI in supporting both the cognitive and affective aspects of learning.]]>
