Artificial intelligence-driven learning assessment in faculties of education: An exploratory study

Amid growing interest in educational technology, there remains a critical gap in understanding how AI-driven assessment is practically implemented in higher education, particularly within faculties of education. This study explores current practices, challenges, and potentials of AI-based assessment tools at the university level. Employing a qualitative exploratory design, data were gathered through semi-structured interviews and institutional document analysis involving 20 faculty members and educational policymakers from various universities in Surakarta municipality, Indonesia. The study investigated how AI tools are applied in formative and summative assessments, including automated feedback, grading, and student performance monitoring. Findings reveal that while AI is appreciated for its efficiency and potential for personalization, significant concerns persist regarding its ability to assess higher-order thinking and complex student outputs. Key challenges include limited infrastructure, insufficient training, and ethical dilemmas such as data privacy and algorithmic bias. Nonetheless, participants acknowledged the transformative potential of AI for real-time, adaptive assessment tailored to individual learner needs. The study highlights the need for thoughtful, context-sensitive integration strategies, emphasizing the role of institutional policy, educator training, and ethical governance. These insights have important implications for developing sustainable, value-driven frameworks for AI assessment in teacher education.