Papers by Kristine Ann M. Carandang
PathBuilder: A Quality-Controlled LLM System for Personalized Learning Pathways (2026.acl-demo)
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Jasper Meynard Arana, John Andrew Mañacop, John Allen Manacop, Roy Andrew Garcia, Keith Rick Piniera, Kristine Ann M. Carandang, Ethan Robert Casin, Christian Alis, Christopher Monterola
| Challenge: | Large language models (LLMs) enable scalable content generation for personalized learning, but reliability and pedagogical alignment remain open challenges. |
| Approach: | They propose a web-based system that integrates expert-validated assessment, retrieval-augmented generation (RAG), and an LLM-as-a-Judge validation loop within a closed instructional pipeline. |
| Outcome: | The proposed system achieves a gain of 37.9 percentage points and a large effect size in a real-world deployment with 179 registered users. |
When Models Hesitate: Answer Instability as a Label-Free Uncertainty Signal for LLMs (2026.acl-srw)
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Jasper Meynard Arana, Kristine Ann M. Carandang, Ethan Robert Casin, Christian Alis, Christopher Monterola
| Challenge: | Existing approaches to uncertainty estimation typically require access to internals, additional supervision, or computationally intensive pipelines. |
| Approach: | They propose to use a label-free uncertainty signal to predict the variability of a model's final answer across repeated stochastic generations of the same prompt to achieve performance competitive with semantic entropy. |
| Outcome: | The proposed method achieves performance competitive with semantic entropy while requiring no similarity model. |
Foundations of PEERS: Assessing LLM Role Performance in Educational Simulations (2025.acl-srw)
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Jasper Meynard Arana, Kristine Ann M. Carandang, Ethan Robert Casin, Christian Alis, Daniel Stanley Tan, Erika Fille Legara, Christopher Monterola
| Challenge: | In education, peer instruction is widely recognized as an effective active learning strategy, but evaluations of PI are limited by logistical constraints and variability in classroom settings. |
| Approach: | They propose a simulation framework that integrates Agent-Based Modeling, Large Language Models, and Bayesian Knowledge Tracing to emulate student learning dynamics. |
| Outcome: | The proposed framework integrates Agent-Based Modeling, Large Language Models, and Bayesian Knowledge Tracing to emulate student learning dynamics in real classrooms. |
Are LLMs reliable? An exploration of the reliability of large language models in clinical note generation (2025.acl-industry)
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Kristine Ann M. Carandang, Jasper Meynard Arana, Ethan Robert Casin, Christopher Monterola, Daniel Stanley Tan, Jesus Felix B. Valenzuela, Christian Alis
| Challenge: | Clinical note generation (CNG) tools are being developed to address extended working hours and healthcare provider fatigue. |
| Approach: | They evaluate the reliability of 12 open-weight and proprietary LLMs from Anthropic, Meta, Mistral, and OpenAI in CNG in terms of their ability to generate notes that are string equivalent (consistency rate), have the same meaning (semantic consistency) and are correct (symbol similarity) |
| Outcome: | The results show that the LLMs generated notes that are string equivalent (consistency rate), have the same meaning (semantic consistency) and are correct (symbol similarity) overall, Meta’s Llama 70B was the most reliable, followed by Mistral’s Small model. |