<![CDATA[Background/ purpose: Learning evaluation is an important component in the education process because it serves to understand the extent to which students comprehend the material they have learned. However, conventional evaluation methods are often still general and have not been able to accommodate differences in students' abilities, learning styles, and individual needs. This presents a particular challenge, especially for non-formal educational institutions that have students with diverse backgrounds. Rumah Pintar Cahaya in Jember Regency, as a non-formal educational institution, plays a role in providing learning access for the community with various levels of academic ability. The diversity of students' backgrounds requires a more flexible and responsive evaluation system. Therefore, the implementation of IA in learning comprehension evaluation becomes relevant to examine. IA has the potential to provide quick feedback, analyze students' learning difficulties, and helping educators design more targeted learning strategies. Method: This study uses a descriptive qualitative approach to examine in depth the role of evaluating students' learning comprehension using AI in Rumah Belajar in Jember Regency. The research subjects were determined purposively based on the evaluation of learning comprehension using AI, meeting the criteria of being a community-based school and providing services to students at Rumah Belajar Cahaya in Jember Regency. The research informants consist of the founders/managers as well as the students of Rumah Belajar Cahaya in Jember Regency. Data was obtained through in-depth interviews, participatory observation, and document analysis to capture authentic experiences, learning practices, and the conditions of school administration comprehensively. Findings: The implementation of AI makes the assessment process more efficient and systematic. AI-based systems can process evaluation results automatically, reducing the time needed to assess students' understanding. AI provides deeper understanding analysis. Evaluations not only produce final scores but also map out areas of the material that students have not yet mastered. There is an increase in student participation in the evaluation process. The use of technology in assessments encourages students to be more active and enthusiastic because they receive immediate feedback. AI helps accommodate differences in learning abilities. The system can adjust the difficulty level of questions and provide learning recommendations according to each student's needs. The workload of educators in the assessment process is reduced. With an automated system, educators can focus more on guidance and reinforcement of the material. Technical obstacles in implementation are still encountered The challenges that arise include limitations in technological facilities and the digital skills of some users. Overall, the implementation of AI contributes positively to the quality of learning assessment. This system supports a more accurate and data-driven learning decision-making process. Solution: Optimization of Supporting Facilities and Infrastructure Institutions need to strengthen learning technology facilities, both in terms of the availability of devices and the quality of internet networks, to support the sustainable implementation of AI-based evaluation systems. Enhancement of Digital Competence for Educators and Learners Capacity-building programs through technical training and regular mentoring are necessary so that all parties involved have adequate understanding in utilizing AI as a learning evaluation tool. Development of Contextual AI-Based Evaluation Models The AI systems used should be designed according to the characteristics of learners in non-formal education settings, so they can accommodate differences in ability levels and learning backgrounds. Implementation of Integrated Evaluation The use of AI in evaluation should be combined with other assessment methods, such as observation and qualitative assessment, to obtain a comprehensive understanding of Students more comprehensively. The implementation of Periodic Monitoring and Reflection on the evaluation of IA system effectiveness needs to be carried out periodically to ensure alignment between learning objectives and the results achieved, as well as serving as a basis for system improvement. Strengthening Ethical and Data Security Aspects. The implementation of IA must consider data protection principles and responsible use of technology, so as not to pose a risk of misuse of student information. Development of Strategic Partnerships. Collaboration with higher education institutions or technology developers can be a strategic step in improving the quality and sustainability of IA-based evaluation systems.]]>
