Larrondo, P. F., & Frank, B. M., & Ortiz, J. (2024, June), Work-in-Progress: Fine-Tuning Large Language Models for Automated Feedback in Complex Engineering Problem-Solving  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--48541

\bibitem{asee_peer_48541}
  Larrondo, P. F., \& Frank, B. M., \& Ortiz, J. (2024, June), \emph{Work-in-Progress: Fine-Tuning Large Language Models for Automated Feedback in Complex Engineering Problem-Solving}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  10.18260/1-2--48541

Paula Francisca Larrondo, Brian M Frank P.Eng., and Julian Ortiz.  "Work-in-Progress: Fine-Tuning Large Language Models for Automated Feedback in Complex Engineering Problem-Solving".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/48541 Internet.  14 Dec, 2024

\bibitem{asee_peer_48541}
  Paula Francisca Larrondo, Brian M Frank P.Eng., and Julian Ortiz.
  "Work-in-Progress: Fine-Tuning Large Language Models for Automated Feedback in Complex Engineering Problem-Solving".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/48541 Internet.  14 Dec, 2024

@INPROCEEDINGS{asee_peer_48541
author = "Paula Francisca Larrondo, Brian M Frank P.Eng., and Julian Ortiz"
title = "Work-in-Progress: Fine-Tuning Large Language Models for Automated Feedback in Complex Engineering Problem-Solving"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/48541}
number = {10.18260/1-2--48541}
}

TY  - CPAPER
AB  - This paper presents work in progress (WIP) toward using artificial intelligence (AI), specifically through Large Language Models (LLM), to support rapid quality feedback mechanisms within engineering educational settings. It describes applying to LLMs to improve the feedback processes by providing information directly to students, graders, or course instructors teaching courses focused on complex engineering problem-solving. We detail how fine-tuning an LLM with a small dataset from diverse problem scenarios achieves classification accuracies close to approximately 80%, even in new problems not included in the fine-tuning process. Traditionally, open-source LLMs, like BERT, have been fine-tuned in large datasets for specific domain tasks. Our results suggest this may not be as critical in achieving good performances as previously thought. Our findings demonstrated the potential for applying AI-supported personalized feedback through high-level prompts incentivizing students to critically self-assess their problem-solving process and communication. However, this study also highlights the need for further research into how semantic diversity and synthetic data augmentation can optimize training datasets and impact model performance.
AU  - Paula Francisca Larrondo
AU  - Brian M Frank P.Eng.
AU  - Julian Ortiz
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Work-in-Progress: Fine-Tuning Large Language Models for Automated Feedback in Complex Engineering Problem-Solving
UR  - https://peer.asee.org/48541
DO  - 10.18260/1-2--48541
ER  -