Core Concepts
Large language models can be effectively modulated by cognitive and noncognitive traits to simulate diverse student profiles, enabling personalized scaffolding strategies in conversational intelligent tutoring systems.
Abstract
This paper proposes a personality-aware simulation framework for conversational intelligent tutoring systems (ITSs), integrating both cognitive and noncognitive aspects to construct student profiles.
The key highlights are:
Cognitive Level Simulation: The framework models students' language abilities across five dimensions - phrases, sentence structure, modifiers, nouns, and verbs. Students with high language ability demonstrate good comprehension, expression, and the ability to create sentences that meet the specified language skills. In contrast, low-ability students struggle with the image description task and forming grammatically correct sentences.
Noncognitive Level Simulation: The authors refine the Big Five personality theory (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) to align with the language learning context, creating the "Big Five for Tutoring Conversation" (BF-TC) scheme. This allows the simulation to capture how students' personality traits influence their engagement, communication, and learning behaviors.
Multi-aspect Validation: The framework is equipped with comprehensive evaluation, including personality categorization, language ability labeling, and scaffolding analysis. This enables measuring the consistency between the simulated student profiles and the intended characteristics, as well as understanding how the tutoring system adapts its strategies based on student traits.
Experimental Results: The authors conduct experiments with four representative large language models (LLMs) - Zephyr, Vicuna, GPT-3.5, and GPT-4. The results show that GPT-4 outperforms other models in generating diverse student responses that align with the specified BF-TC traits and language abilities. The analysis also reveals that the tutoring system adjusts its scaffolding strategies, such as providing more hints and explanations, to accommodate students with lower language proficiency or certain personality traits (e.g., low Openness, Conscientiousness, Extraversion).
Overall, this work demonstrates the potential of leveraging LLMs for personality-aware student simulation, which can facilitate the development and evaluation of personalized conversational ITSs.
Stats
"Students with higher language proficiency receive more positive feedback, instructions, and questions: the teacher provides more affirmations to the responses and encourages students to explore details in the given picture."
"Conversely, students with lower language proficiency may struggle with vocabulary and sentence structure, require support in organizing answers, and they receive more hints, explanations, and modeling."
"The low indicator of Openness, Conscientiousness, and Extraversion results in more hints from the teacher, and Neuroticism is negatively related to all scaffolding strategies except questioning."
Quotes
"LLMs can be modulated by specifying personality traits to simulate different student groups and produce diverse responses, and scaffolding strategies would be adjusted upon student characteristics."
"GPT-4 outperforms other models in generating diverse student responses that align with the specified BF-TC traits and language abilities."