BLCU-ICALL at BEA 2025 Shared Task: Multi-Strategy Evaluation of AI Tutors

AbstractThis paper describes our approaches for the BEA-2025 Shared Task on assessing pedagogical ability and attributing tutor identities in AI-powered tutoring systems. We explored three methodological paradigms: in-context learning (ICL), supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF). Results indicate clear methodological strengths: SFT is highly effective for structured classification tasks such as mistake identification and feedback actionability, while ICL with advanced prompting excels at open-ended tasks involving mistake localization and instructional guidance. Additionally, fine-tuned models demonstrated strong performance in identifying tutor authorship. Our findings highlight the importance of aligning methodological strategy and task structure, providing insights toward more effective evaluations of educational AI systems.