INVESTIGATE HOW AI CAN ANALYZE PROJECT-BASED OR PERFORMANCE-BASED ASSESSMENTS MORE HOLISTICALLY
Keywords:
Project‐Based Assessment, Performance‐Based Assessment, Holistic Assessment, Educational Analytics, Process Data, Collaboration Analytics, Metacognitive ReflectionAbstract
This research investigates the potential for using artificial intelligence (AI) to examine project-based and performance-based evaluations holistically. Conventional assessment methods tend to concentrate on isolated task outcomes (e.g., end product or grade) and do not consider such factors as process, collaboration, creativity, iteration, and reflection on the part of the learner. Through the use of AI-powered analytics on rich data from project-based assessments (PBAs), this study investigates if AI can present worthwhile insights in various dimensions of students' performance—content knowledge, skills, process engagement, and metacognitive reflection. The research followed a mixed-methods study using 200 undergraduate teacher-education students with AI tools processing assessment artefacts (e.g., project outputs, records of student collaboration, process artefacts) and producing analytic reports. Results show that AI can accurately detect patterns of process engagement and collaboration, and that students whose AI-reports of process scores were also higher received more robust performance. Inference for design of assessment, teacher practice, and AI tool design are explored.
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Copyright (c) 2025 Moneeza Aslam, Dr. Saira, Irsa Tayyab (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
