Enhancing Personalized Learning with MBTI Forecasts and ChatGPT's Tailored Study Advice - Abstract and Methods Overview

Sep 17, 2026

Assessing students' learning behaviors has long been central to education, yet conventional grading-centric evaluations often fail to capture underlying needs. This study models learning conditions through Myers-Briggs Type Indicator (MBTI) personality types to enable personalized recommendations. Using the LBLS467 database, students are clustered with K-means, MBTI types are inferred with ChatGPT, and learning advice is generated. Similarity-based peer suggestions support group learning, aiming to improve motivation and learning efficiency in educational settings.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Abstract

Keywords

1. Introduction

2. Methods

2.1 Dataset

2.2 Experiment Design

2.3 Data preprocessing, Cluster Analysis and Finding representatives

# Enhancing Personalized Learning with MBTI Forecastsand ChatGPT's Tailored Study Advice Yi-Chun, Hsieh1 and Albert C.M. Yang1 1Department of Computer Science and Information Engineering, National Chung Hsing University, Taiwan ## Abstract Assessing students' learning behaviors has always been a focal point in the field of education. However,traditional assessment methods based solely on grades and learning behaviors often lackpersonalization, failing to truly understand the root of students' issues. Therefore, this study aims toaddress this problem by focusing on understanding students' Myers-Briggs Type Indicator (MBTI)personality types. It aims to provide personalized recommendations based on students' learningconditions and personality traits. Ultimately, it intends to suggest suitable study companions forstudents,enhancingboththeirlearningmotivationandefficiency. Toachievethisgoal, thisstudyutilizesthe LBLS467 Database to cluster students, conducts MBTI personality assessments using ChatGPT,offers study recommendations, and ultimately compares similarities to suggest suitable peers for grouplearning. This approach aims to aid students in their learning within the educational environment. ## Keywords Learning Behavior, Behavioral Analysis, Student Modeling ## 1. Introduction Personality traits area psychological concept that interprets the meaning behind various humanbehaviors by observing specific characteristics, thus identifying individuals' traits. One of themost popular methods recently isthe Myers-Briggs Type Indicator (MBTI) personality analysis.Using machine learning techniques to analyze comments or tweets on social media to predict theauthor's MBTI personality type lays the foundation for creating a system that identifies people'spersonalities [1] . Questionnaires are widely regarded as effective tools for data collection in various fields. Byanalyzing questionnaires, one can identify data distributionsand consequently acquire resourcesthat align with their objectives [2] . conducted an analysis using discrete data collected throughquestionnaires, exploring the impact of variable combinations in questionnaire responses onbehavior. Developed a model applicable to various questionnaire datasets for conducting clusteranalysis and interpretable inference [3] . Utilized Kmeans clustering analysis on questionnairedata during experimentation, resulting in the creation of an MBTI personality prediction system[4] . After conducting a learning analysis using questionnaires, personalized learningrecommendations and teaching guidance can be provided to enhance students' learningefficiency and motivation. Utilized machine learning to analyze student learning data and offeringfeedback that enables students to self-reflect on their learning activities and promote self-regulation [5] . In addition to a strong learning motivation, peer-assisted learning is also crucial. Conducted anMBTI analysis on employees' community posts to establish their personality traits. This led to theformation of optimal team compositions, creating the most suitable work teams based onidentified personality characteristics [6] . Therefore, this study utilized the LBLS467 learning questionnaire and conducted analysis usingKmeans to determine students' MBTI classifications. Based on this, appropriate learning advicewas provided. Additionally, recommendations were made for students to engage in mutuallearning to achieve collaborative learning objectives. Ultimately, this approach aimed to enhancestudents'learning motivation and efficiency. LAK-WS 2024: Joint Proceedings of LAK 2024 Workshops, March 18–19, Kyoto, Japan talia@smail.nchu.edu.tw (Yi-Chun, Hsieh); albertyang1994@gmail.com (Albert C.M. Yang) © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) . CEUR Workshop ceur-ws.org Proceedings ISSN 1613-0073 ## 2. Methods ### 2.1 Data

FAQs

Why does the study move beyond traditional assessment methods?

Traditional approaches based mainly on grades and surface learning behaviors lack personalization and do not reveal the root causes of student issues. The study addresses this by incorporating personality-driven analysis.

What data sources are used in the proposed method?

The study uses LBLS467, including the Strategy Inventory of Language Learning (SILL), as well as self-regulated learning questionnaires (SRL_S) and motivation questionnaire items (SRL_M).

How are MBTI types and study recommendations produced?

Students are clustered via K-means, and representatives from each cluster are used with ChatGPT to determine MBTI types. ChatGPT then generates learning recommendations, followed by suggesting suitable study partners based on matching logic.

Sep 17, 2026

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