Quantifying learning-style adaptation in effectiveness of LLM teaching (2024)

Abstract: This preliminary study aims to investigate whether AI, when prompted based on individual learning styles, can effectively improve comprehension and learning experiences in educational settings. It involves tailoring LLMs baseline prompts and comparing the results of a control group receiving standard content and an experimental group receiving learning style-tailored content. Preliminary results suggest that GPT-4 can generate responses aligned with various learning styles, indicating the potential for enhanced engagement and comprehension. However, these results also reveal challenges, including the model’s tendency for sycophantic behavior and variability in responses. Our findings suggest that a more sophisticated prompt engineering approach is required for integrating AI into education (AIEd) to improve educational outcomes.

## introduction students have recently been exposed to the remarkable capabilities of generative ai (ai) in education. notably, openai's chatgpt, one of the most capable large language models (llm) @xcite , has been successfully used as a versatile assistant for teaching staff and a digital tutor for higher education students @xcite . chatgpt is appreciated by both instructors and students for its ability to explain complex problems that would have necessitated substantial help and detailed feedback from traditional classroom resources. however, where human teachers can adapt to individuals' needs, chatgpt provides generic outputs that do not cater to stimulating better learning. it is also designed to please users, which could lead to sycophantic behavior @xcite . it is crucial for the future of education that we extract maximal benefits from generative ai, rather than using it as an intelligent search engine that answers students' questions without confirming if they understand and retain the information they learned. a potential solution is improvement by prompting llms based on studied pedagogical concepts, such as using learning styles that improve educational outcomes in an offline setting. we hypothesize that, if learning styles are effective for comprehension, then learning-style-based prompting should increase student understanding as a result. in the pursuit of more effective ai teaching, this study aims to show the potential benefits of an individual approach to educational prompting. this research provides a comprehensive framework for evaluating the effectiveness of llms at adapting teaching styles to increase student comprehension. it aligns with the overall objective of developing a system that can automatically refine their teaching based on student outcomes and feedback. more importantly, our work investigates whether llms can tailor their teaching style in reaction to prompting instructions to promote interactive learning outcomes. this study thus marks a first step towards achieving a comprehensive individualized approach for ai-assisted education. ## literature review the integration of ai into education (aied) has recently emerged as a significant area of research. however, concerns have been raised about their potential ability to teach effectively and contribute to improving educational outcomes. in a systematic literature review, @xcite synthesized 113 papers across disciplines to identify key opportunities and challenges in ai's role in education. one of the main conclusions of this review is that researchers should develop ethical and inclusive aied systems to promote pedagogically sound and evidence-based aied interventions for increasing teacher and student agency and engagement. @xcite examined news articles (@xmath0. their conclusion is that universities should focus on teaching students how to effectively use chatgpt in ways that improve learning (see also @xcite . one way to stimulate learning is by prompting chatgpt to not directly answer students' questions. rather, @xcite argue that it is more effective to combine direct instruction with inquiry when explaining new concepts in a learning environment. looking deeper into the pedagogical benefits and risks of different ai interventions in classrooms, @xcite suggest several teaching strategies that can help student learning in the presence of ai while mitigating potential risks. their research emphasizes the importance of maintaining human involvement in the educational process, and positioning ai as a supportive tool, as opposed to a substitute for human instructors. ai tools can also enhance teaching and learning experiences by supporting personalized and interactive learning, generating prompts for formative assessment, and enhancing creativity and critical thinking @xcite . the previous studies emphasize the role that ai can play in improving teaching and learning experiences. they also suggest that diverse learning preferences are important for the effective integration of ai into educational strategies. however, the integration of these technologies with specific learning styles remains limited, mainly because they rely on older ai models that do not have the ability to develop sophisticated customizable pedagogical approaches @xcite . this gap represents an opportunity for researchers to develop ai tools that are not only technologically advanced, but also pedagogically sound, to match the unique needs and preferences of students. we present a more in-depth discussion of the literature on different learning styles, their theoretical foundations, influence on education, and the potential synergy between ai tools and tailored learning approaches in appendix a. ## methodology this section describes the design and development of an ai-assisted learning system aimed at enhancing the learning experience for students by tailoring prompts and interactions based on their learning styles. from the literature, we identified four specific learning styles that we further describe in appendix ??. these are: diverger/reflector (learn by experiencing), assimilator/theorist (learn by conceptualizing), converger/pragmatist (learn by applying), accommodator/activist (learn by creating). ## preliminary results although our findings are preliminary and do not yet include the main experimental data, we can infer implications by analyzing the responses observed during the prompting phase and our understanding of the learning styles. our results suggest that gpt-4 can be prompted to generate responses based on the description of specified learning styles. examples of these responses have been included in appendix c. for instance, for type 1 learners, who prefer to work in situations that require generating ideas and are comfortable in unstructured 'real-world' scenarios, the output encourages learners to explore and apply concepts in a practical context by emphasizing experiential learning. for type 2 learners, who thrive in structured, theoryoriented environments, the output focuses on stepby-step conceptual understanding before applying it, which is the ideal approach for learners who prefer structured learning. for type 3 learners, who like to apply ideas to find practical solutions, the output blends theory with practical application by guiding learners through the process. finally, for type 4 learners, who prefer active experimentation and adaptability in learning scenarios, the output emphasizes problem-solving and the creation of solutions by encouraging active experimentation and innovation. each prompt was tested 25 times for single output to the sample question b in individual trials to evaluate the consistency and alignment of gpt-4's responses with the intended learning style. to ensure that the instructions provided remained comparable across queries, we conducted an analysis to measure the consistency of the output generated by the model. this analysis of variability was as follows. first, we began by creating an ada model text-based embedding with the aim of quantifying semantic meaning in 25 model outputs per each of the four learning style to sample question b @xcite . we subsequently conducted a cluster analysis on these scores to determine whether the text generated by the learning style prompts formed well-defined clusters. visualization of the text embedding results are shown in appendix d. our findings suggest that they do not: the ai's ability to generate distinct content based on learning styles is not as well-defined as initially anticipated. second, we tasked gpt-4 with classifying each of the 100 unlabeled outputs to one of the four learning styles. the classification provided also suggests inconsistency in distinguishing among the learning styles. the results from this last analysis are presented in ## discussion and limitations the previous analysis suggests that there is a need for further research and development in the realm of educational prompting. despite observing general trends that align with the intended learning styles, the next stage of this study will focus on addressing these challenges and gathering more substantial evidence. moving forward, we suggest fine-tuning gpt-4 models using the new custom gpt tool from openai, which are models that we can customize specifically to the learning styles @xcite . we expect these fine-tuned mod-els to adapt more precisely educational prompts. we also plan to conduct experimental tests to determine whether educational outcomes improve when students are presented with content tailored to their learning styles. because the use of ai, particularly llms, in an educational context is a relatively new phenomenon, we need to conduct more extensive evaluations of their capabilities to improve learning outcomes. while the models show promise in delivering tailored instructions, our understanding of their efficacy compared to traditional teaching methods remains limited. the tendency to display sycophantic behavior, especially in situations where it should correct incorrect student answers, represents a significant challenge that needs to be addressed in future phases of this study to ensure the integrity and educational value of ai-assisted learning. the variability in responses observed in our experiments also suggests a level of randomness in gpt-4's outputs. this raises important questions about the reliability of the model; this also suggests that more sophisticated prompt engineering or fine-tuning is needed to prevent this type of behavior from the model. future work should also aim to include larger and more diverse participant groups, longer intervention periods, and a broader range of educational content to better assess the impact of learning style-tailored prompts. however, it is essential to acknowledge the criticisms surrounding learning styles assessments. @xcite argue that most tests intended to determine a person's learning style fail to satisfy all of the minimum psychometric criteria, including internal consistency, test-retest reliability, construct validity, and predictive validity. @xcite further suggests that self-reporting tests may be unreliable since learners may be either unable or unwilling to accurately report their preferences. in light of these criticisms, it is important to recognize that learning styles are not fixed or deterministic, and educators should consider diverse variables such as prior knowledge, motivation, interests, and feedback when accommodating individual needs @xcite . while criticisms surrounding the determination and application of learning styles persist, when considered as part of a broader array of factors, learning styles can still provide valuable insights into students' preferences, motivations, and cognitive processes @xcite . learning styles should not be regarded as the only approach to improve educational outcomes. nonetheless, when integrated into learning strategies, they can serve as valuable components in the broader toolkit of pedagogical strategies @xcite .