Analysis of University Students' Current Level and Importance in ChatGPT Using Borich Needs and The Locus for Focus Model

Abstract

Objectives The purpose of this study is to analyze the perception level of university students regarding ChatGPT using the Borich Needs and The Locus for Focus model. Methods For this purpose, a total of 199 undergraduate students from University A in the metropolitan area were selected as participants, and the research was conducted using SPSS 25.0 and Excel software programs. Statistical significance was analyzed using t-tests to examine the differences between the current level and im-portance in IPA analysis.Borich'sNeeds and The Locus for Focus Model were examined in a 2x2 matrix, divided into four quadrants. Results The key findings of this study are as follows: First, in the IPA analysis, the reinforcement and maintenance area(HH) revealed 2 items related to understanding ChatGPT, 5items related to ChatGPT principles and applica-tions, and 1 item related to data and machine learning. The concentration management area(HL) included 1 item related to ChatGPT principles and applications, and 3 items related to data and machine learning. The low priority area(LL) consisted of 1 item related to understanding ChatGPT, 2 items related to ChatGPT principles and applica-tions, and 4 items related to social impact. The excessive area(LH) was characterized bybiaseddata in the social impact category. Second, it was found that the differences between importance and current level were higher in terms of importance for all items, except for those related to data bias. This difference was statistically significant. The priority order according to Borich's needs includes 'structuredand unstructured data', 'sensorsand perception', 'computer vision', 'concepts of AI agents' and 'conceptsand application learning of machines'. Third, The Locus for Focus model analysis revealed that the HH area consisted of 9 items, including 'speech recognition and language understanding', 'problem solving and exploration', 'representation and deduction of information', 'concepts and ap-plication learning of machines', 'concepts and application of artificial neural networks', 'data attributes', 'structured and unstructured data', 'classification models', and 'machine learning model implementation'. Fourth, a total of 6 items, including 'speech recognition and language understanding', 'representation and deduction of information', 'concepts and application learning of machines', 'concepts and application of artificial neural networks', 'structured and unstructured data', and 'classification models' were consistently rated as high priority and demanded the high-est attention. Conclusions The findings of this study indicate the need to explore ways to apply ChatGPT in education. Furthermore, it proposes a long-term education plan to enhance awareness of the social impact of ChatGPT and provide practical strategies for learning and applying it.