Copertina

Supervised Fine-Tuning LLMs to Behave as Pedagogical Agents in Programming Education

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ross, Emily, Kansal, Yuval, Renzella, Jake, Vassar, Alexandra, Taylor, Andrew
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language Computers and Society
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education
di: Vassar, Alexandra, et al.
Pubblicazione: (2024)

Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools
di: Solano, Lorenzo Lee, et al.
Pubblicazione: (2025)

Scaling CS1 Support with Compiler-Integrated Conversational AI
di: Renzella, Jake, et al.
Pubblicazione: (2024)

AI Literacy, Safety Awareness, and STEM Career Aspirations of Australian Secondary Students: Evaluating the Impact of Workshop Interventions
di: Bergh, Christian, et al.
Pubblicazione: (2026)

FoundationalASSIST: An Educational Dataset for Foundational Knowledge Tracing and Pedagogical Grounding of LLMs
di: Worden, Eamon, et al.
Pubblicazione: (2026)

Passing the Turing Test in Political Discourse: Fine-Tuning LLMs to Mimic Polarized Social Media Comments
di: Pazzaglia, ., et al.
Pubblicazione: (2025)

MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education
di: Liu, Naiming, et al.
Pubblicazione: (2024)

Learning from Convenience Samples: A Case Study on Fine-Tuning LLMs for Survey Non-response in the German Longitudinal Election Study
di: Holtdirk, Tobias, et al.
Pubblicazione: (2025)

Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning
di: Singh, Navan Preet, et al.
Pubblicazione: (2026)

Measuring Fine-Grained Negotiation Tactics of Humans and LLMs in Diplomacy
di: Li, Wenkai, et al.
Pubblicazione: (2025)

SFT-TA: Supervised Fine-Tuned Agents in Multi-Agent LLMs for Automated Inductive Thematic Analysis
di: Yi, Seungjun, et al.
Pubblicazione: (2025)

Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks
di: Møller, Anders Giovanni, et al.
Pubblicazione: (2024)

MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions
di: Preniqi, Vjosa, et al.
Pubblicazione: (2024)

LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education
di: Weissburg, Iain, et al.
Pubblicazione: (2024)

LLMs Provide Unstable Answers to Legal Questions
di: Blair-Stanek, Andrew, et al.
Pubblicazione: (2025)

Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs
di: Kansal, Yuval, et al.
Pubblicazione: (2025)

Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profiles
di: Abdulsalam, Ramatu Oiza, et al.
Pubblicazione: (2025)

Striking a Balance between Classical and Deep Learning Approaches in Natural Language Processing Pedagogy
di: Joshi, Aditya, et al.
Pubblicazione: (2024)

Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues
di: Ikram, Fareya, et al.
Pubblicazione: (2025)

Generalization to Political Beliefs from Fine-Tuning on Sports Team Preferences
di: Terry, Owen
Pubblicazione: (2026)

Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection
di: Wang, Bing, et al.
Pubblicazione: (2026)

Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems
di: Harvey, Emma, et al.
Pubblicazione: (2025)

Using LLMs for Knowledge Component-level Correctness Labeling in Open-ended Coding Problems
di: Duan, Zhangqi, et al.
Pubblicazione: (2026)

Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluations
di: Hou, Abe Bohan, et al.
Pubblicazione: (2024)

Large Language Models (LLMs) as Agents for Augmented Democracy
di: Gudiño-Rosero, Jairo, et al.
Pubblicazione: (2024)

The Statistical Signature of LLMs
di: Hadad, Ortal, et al.
Pubblicazione: (2026)

Findings of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors
di: Kochmar, Ekaterina, et al.
Pubblicazione: (2025)

Interpreting Latent Student Knowledge Representations in Programming Assignments
di: Fernandez, Nigel, et al.
Pubblicazione: (2024)

HugAgent: Benchmarking LLMs for Simulation of Individualized Human Reasoning
di: Li, Chance Jiajie, et al.
Pubblicazione: (2025)

GRILE: A Benchmark for Grammar Reasoning and Explanation in Romanian LLMs
di: Dumitran, Adrian-Marius, et al.
Pubblicazione: (2025)

Leveraging Prompts in LLMs to Overcome Imbalances in Complex Educational Text Data
di: McClure, Jeanne, et al.
Pubblicazione: (2024)

Benchmarking Educational Program Repair
di: Koutcheme, Charles, et al.
Pubblicazione: (2024)

Taxonomizing Representational Harms using Speech Act Theory
di: Corvi, Emily, et al.
Pubblicazione: (2025)

We're Different, We're the Same: Creative Homogeneity Across LLMs
di: Wenger, Emily, et al.
Pubblicazione: (2025)

Tracking the Temporal Dynamics of News Coverage of Catastrophic and Violent Events
di: Lugos, Emily, et al.
Pubblicazione: (2026)

Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs
di: Scarlatos, Alexander, et al.
Pubblicazione: (2024)

Partnering with AI: A Pedagogical Feedback System for LLM Integration into Programming Education
di: Scholz, Niklas, et al.
Pubblicazione: (2025)

Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models
di: Chanenson, Jake, et al.
Pubblicazione: (2023)

Zoning in American Cities: Are Reforms Making a Difference? An AI-based Analysis
di: Salazar-Miranda, Arianna, et al.
Pubblicazione: (2025)

Programming in Brazilian Higher Education and High School: A Systematic Literature Review
di: Gonçalves, Sofia C. Latini, et al.
Pubblicazione: (2025)