May 30 ~ 31, 2026, Virtual Conference
Catherine Haliotou, Greece
School career guidance is most commonly conceptualized as a decision-making support mechanism oriented toward facilitating educational and vocational choices through information, assessment, and planning. While such approaches may be effective in adult guidance contexts, their uncritical transfer to school settings raises substantial conceptual and pedagogical concerns. Schools are not spaces of decision optimization but institutions of education, formation, and socialization, within which developmental processes unfold over time. This paper reconceptualizes school career guidance as an educational practice embedded in the pedagogical mission of schooling. Drawing on the theoretical work of Spiros Krivas, it advances an epistemology of formation in which guidance is understood as a mediated, developmental process supporting meaning-making, reflexivity, and students’ evolving relationships to learning and future trajectories. The paper situates this framework within international guidance theory through critical dialogue with career construction, life design, and policy-driven approaches. It further examines guidance as a site of educational power, addressing normalization, regulation, and emancipatory potential, and outlines methodological implications for educational research that resist simplistic outcome-based metrics.
School Career Guidance, Educational Practice, Epistemology of Formation, Pedagogical Mediation, Educational Power.
John Hedlund-Fay, University of Sheffield, UK
Enterprise NL-to-SQL generation remains brittle in high-compliance environments where query correctness depends not only on schema definitions but on external, prescriptive regulatory logic. While Retrieval-Augmented Generation (RAG) of ers a theoretical solution, its ef icacy in bridging this "Semantic Gap" remains under-explored. We present a benchmark of Natural Language prompts within the professional football domain and conduct a comparative analysis between a modular decomposition pipeline (RAG-R) and a direct agentic context-augmentation architecture (RAG-C). Results indicate that while RAG-C underperformed the best non-RAG baseline (ten-shot CoT), RAG-R achieved superior performance. Notably, RAG-R outperformed the CoT baseline by 0.116 in average Exact Set Match (EM) and showed a 0.278 EM gain for the highest-dif iculty domain-specific queries. These findings demonstrate the importance of task decomposition when applying RAG to complex, jargon-heavy SQL generation tasks, specifically in legalistic enterprise environments where query correctness relies on external regulatory logic rather than just schema knowledge.
NL-to-SQL, Retrieval Augment-Generation (RAG), Modular Decomposition, Benchmark Construction, Domain-Specific Knowledge (DSK), Enterprise NLP