Human-AI Collaboration in Assessment

This research examines human-AI collaborative assessment in education, investigating how AI technologies can complement human judgment in evaluation practices. Using a mixed-methods approach, we analyzed experimental implementations of AI-assisted grading systems and gathered qualitative insights from educators, students, and AI developers. Results show that well-designed collaborative models can enhance evaluation validity, reduce workload, increase assessment frequency, and provide more timely feedback. However, challenges persist including algorithmic bias, domain-specific calibration needs, and tensions between standardization and personalization. The study concludes that effective human-AI assessment partnerships require thoughtful role distribution, transparent design, continuous validation, and appropriate professional development. This work contributes to AI integration discourse by proposing a framework that maintains human judgment as the central authority while leveraging AI capabilities as enhancement tools rather than replacements