@inproceedings{jain-rengarajan-2025-emergent,
    title = "Emergent Wisdom at {BEA} 2025 Shared Task: From Lexical Understanding to Reflective Reasoning for Pedagogical Ability Assessment",
    author = "Jain, Raunak  and
      Rengarajan, Srinivasan",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.bea-1.86/",
    pages = "1108--1120",
    ISBN = "979-8-89176-270-1",
    abstract = "For the BEA 2025 shared task on pedagogi- cal ability assessment, we introduce LUCERA (Lexical Understanding for Cue Density{--}Based Escalation and Reflective Assessment), a rubric-grounded evaluation framework for sys- tematically analyzing tutor responses across configurable pedagogical dimensions. The ar- chitecture comprises three core components: (1) a rubric-guided large language model (LLM) agent that performs lexical and dialogic cue extraction in a self-reflective, goal-driven manner; (2) a cue-complexity assessment and routing mechanism that sends high-confidence cases to a fine-tuned T5 classifier and esca- lates low-confidence or ambiguous cases to a reasoning-intensive LLM judge; and (3) an LLM-as-a-judge module that performs struc- tured, multi-step reasoning: (i) generating a domain-grounded reference solution, (ii) iden- tifying conceptual, procedural and cognitive gaps in student output, (iii) inferring the tutor{'}s instructional intent, and (iv) applying the rubric to produce justification-backed classifications. Results show that this unique combination of LLM powered feature engineering, strategic routing and rubrics for grading, enables com- petitive performance without sacrificing inter- pretability and cost effectiveness."
}