<![CDATA[The lack of understanding and interest among students in learning science and the scarcity of energy are very interesting topics to study using case-based learning. The novelty lies in the use of Case Based Learning and Artificial Intelligence related to renewable energy. The objectives of the study are (1) to identify the Hypothetical Learning Trajectory (HLT) of renewable energy based on learning barriers, and (2) to measure scientific process skills and digital literacy. The sample consisted of 120 high school students in Lubuklinggau. The research design was a Lesson Study approach. Implementation occurred over two cycles in collaboration with teachers to design renewable energy learning materials, including lesson plans (RPP), Student Worksheets (LKPD) 1 and 2. AI tools used included Padlet, Kahoot, and Cap Cut. Data collection involved documentation, observation, and interviews. Data analysis was conducted using quantitative descriptive methods. Learning difficulties were identified in students' inability to conduct investigations, analyze data, and communicate graphs effectively. LKPD 1 and 2 were found to be highly valid with a validity test score of 0.92, making them suitable for use. Additionally, there was an increase in students' average science process skills by 0.87, categorized as high, and digital literacy by 0.88, categorized as highly skilled. Its contribution lies in integrating digital LKPD that is easy to access and engaging due to AI integration. Furthermore, the LKDP developed involves students conducting experiments and field observations to measure the efficiency of biomass stoves fueled by rice husks, wood chips, and LPG stoves. Another interesting finding is that students with low abilities experienced a very high increase in digital literacy. The next step is to develop diagnostic test instruments for science literacy, digital literacy, and learning modalities.]]>
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