ARTIFICIAL INTELLIGENCE IN ACCOUNTING EDUCATION: IDENTIFYING LEARNING STYLES AND ASSESSING INDIVIDUAL DIFFERENCES

Fevzi Serkan Özdemir; Haluk Bengü; Eda Turan

Makale detayları

ARTIFICIAL INTELLIGENCE IN ACCOUNTING EDUCATION: IDENTIFYING LEARNING STYLES AND ASSESSING INDIVIDUAL DIFFERENCES

Yazarlar:

Fevzi Serkan Özdemir | Haluk Bengü | Eda Turan

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Ekonomi | İşletme ve Yönetim Müfredatı ve Öğretimi

Dergi:

Niğde Ömer Halisdemir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Vol.6 Issue 2, pp.417-436

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336521

pISSN:

2687-5306

Anahtar kelimeler:

Özet:

 Evaluations on the role of artificial intelligence AI in education emphasize its potential contributions to student-centered learning and personalized education. However, while studies have begun to explore the expected contributions of these relatively new AI applications, comparative differencesspecifically performance assessmentsbetween AI usage and direct human effort are not yet sufficiently developed. Although there are limited studies aimed at determining learning styles through the use of AI, their consistency with actual results is not thoroughly examined. This study aims to assess the individual differences of accounting students at a vocational and technical high education school using the Kolb Learning Style Inventory KLSI and to evaluate the performance consistency of AI applications ChatGPT, Gemini, and Copilot against actual implementations. To this end, responses from 11 vocational and technical high school accounting students, whose learning styles were previously determined using KLSI, were utilized. Three different AI tools were instructed to determine the learning styles of these students using the same commands. In this way, the effectiveness of AI tools in identifying and assessing individual differences among students was examined both independently and comparatively. According to the findings, ChatGPT showed the highest performance, with only one incorrect assessment, while the other AIs made three incorrect assessments. Notably, the observation that ChatGPT incorrectly identified did not overlap with the incorrect observations of the others. In contrast, two of the three incorrect assessments by Gemini and Copilot pertained to the same two observations. Based on all the findings, this study, which provides an initial evaluation of the performance of AI in meeting the expected contributions and, specifically, in using KLSI, suggests that while AI can facilitate the identification and evaluation of individual differences in teaching, the possibility of errors should not be overlooked. Essentially, the study, with its empirical evidence, highlights that AIs still need to continue learning themselves and that relying solely on AI in zero-tolerance-required tasks, such as identifying students individual characteristics, could be risky.

 Evaluations on the role of artificial intelligence AI in education emphasize its potential contributions to student-centered learning and personalized education. However, while studies have begun to explore the expected contributions of these relatively new AI applications, comparative differencesspecifically performance assessmentsbetween AI usage and direct human effort are not yet sufficiently developed. Although there are limited studies aimed at determining learning styles through the use of AI, their consistency with actual results is not thoroughly examined. This study aims to assess the individual differences of accounting students at a vocational and technical high education school using the Kolb Learning Style Inventory KLSI and to evaluate the performance consistency of AI applications ChatGPT, Gemini, and Copilot against actual implementations. To this end, responses from 11 vocational and technical high school accounting students, whose learning styles were previously determined using KLSI, were utilized. Three different AI tools were instructed to determine the learning styles of these students using the same commands. In this way, the effectiveness of AI tools in identifying and assessing individual differences among students was examined both independently and comparatively. According to the findings, ChatGPT showed the highest performance, with only one incorrect assessment, while the other AIs made three incorrect assessments. Notably, the observation that ChatGPT incorrectly identified did not overlap with the incorrect observations of the others. In contrast, two of the three incorrect assessments by Gemini and Copilot pertained to the same two observations. Based on all the findings, this study, which provides an initial evaluation of the performance of AI in meeting the expected contributions and, specifically, in using KLSI, suggests that while AI can facilitate the identification and evaluation of individual differences in teaching, the possibility of errors should not be overlooked. Essentially, the study, with its empirical evidence, highlights that AIs still need to continue learning themselves and that relying solely on AI in zero-tolerance-required tasks, such as identifying students individual characteristics, could be risky.

 Evaluations on the role of artificial intelligence AI in education emphasize its potential contributions to student-centered learning and personalized education. However, while studies have begun to explore the expected contributions of these relatively new AI applications, comparative differencesspecifically performance assessmentsbetween AI usage and direct human effort are not yet sufficiently developed. Although there are limited studies aimed at determining learning styles through the use of AI, their consistency with actual results is not thoroughly examined. This study aims to assess the individual differences of accounting students at a vocational and technical high education school using the Kolb Learning Style Inventory KLSI and to evaluate the performance consistency of AI applications ChatGPT, Gemini, and Copilot against actual implementations. To this end, responses from 11 vocational and technical high school accounting students, whose learning styles were previously determined using KLSI, were utilized. Three different AI tools were instructed to determine the learning styles of these students using the same commands. In this way, the effectiveness of AI tools in identifying and assessing individual differences among students was examined both independently and comparatively. According to the findings, ChatGPT showed the highest performance, with only one incorrect assessment, while the other AIs made three incorrect assessments. Notably, the observation that ChatGPT incorrectly identified did not overlap with the incorrect observations of the others. In contrast, two of the three incorrect assessments by Gemini and Copilot pertained to the same two observations. Based on all the findings, this study, which provides an initial evaluation of the performance of AI in meeting the expected contributions and, specifically, in using KLSI, suggests that while AI can facilitate the identification and evaluation of individual differences in teaching, the possibility of errors should not be overlooked. Essentially, the study, with its empirical evidence, highlights that AIs still need to continue learning themselves and that relying solely on AI in zero-tolerance-required tasks, such as identifying students individual characteristics, could be risky.

 Yapay zekânın eğitimdeki rolüne dair değerlendirmelerde öğrenci merkezli öğrenme ve özelleştirilmiş eğitim konularında sağlayacağı katkılara vurgu yapılmaktadır. Fakat henüz çok yeni olan yapay zekâ uygulamalarının sağlaması beklenen katkılar konusunda çalışmalar yapılmaya başlanmıştır. Ancak, yapay zekâ kullanımı ile doğrudan insan gücü kullanımı arasındaki mukayeseli farklar başarım değerlendirmeleri aynı düzeyde değildir. Özelde yapay zekâ kullanımıyla öğrenme stillerinin belirlenmesi amacıyla yapılan sınırlı sayıda çalışma bulunsa da, bunların gerçek fiili sonuçlarla olan tutarlılığı ele alınmamaktadır. Çalışmanın amacı da Kolb Öğrenme Stilleri Envanterini KÖSE kullanarak bir mesleki ve teknik eğitim lisesindeki muhasebe öğrencilerinin bireysel farklılıklarını tespit etme ve değerlendirmede yapay zekâ Chat GPT, Gemini ve Copilot uygulamalarının fiili uygulama karşısındaki başarımını tutarlılığını değerlendirmektir. Bunun için daha önce KÖSE ile öğrenme stilleri fiili olarak belirlenen 11 mesleki ve teknik eğitim lisesi öğrencisinin verdiği cevaplardan yararlanılmış ve üç farklı yapay zekâ aracından, aynı komutlandırmayla ilgili öğrencilerin öğrenme stillerini belirlemeleri istenmiştir. Bu suretle yapay zekâların öğrencilerin bireysel farklılıklarını tespit etme ve değerlendirme konusundaki işlevi ne derece doğru ve etkili başarılı bir şekilde yerine getirebildikleri hem münferit hem de karşılaştırmalı olarak ele alınmıştır. Elde edilen bulgulara göre en yüksek başarım ChatGPTnin olmuştur. ChatGPTnin hatalı tespit sayısı 1 iken diğer yapay zekâların hatalı tespit sayısı 3tür. Ancak burada dikkat çeken husus; ChatGPTnin hatalı olarak tespit ettiği gözlemin, diğerlerinin hatalı gözlemleriyle uyuşmamasıdır. Buna karşın Gemininin ve Copilotun 3 hatalı tespitinin 2si aynı iki gözleme aittir. Elde edilen tüm bulgular doğrultusunda, yapay zekâların kendilerinden beklenen katkının başarımı ve özelde de KÖSE yönüyle yapay zekâ kullanımının başarımına dair ilk değerlendirmeyi barındıran bu çalışmayla, öğretimde yapay zekâ kullanımının bireysel farklılıkları tespit etme ve değerlendirmede konusunda kolaylık sağladığı ifade edilebilirse de, hata ihtimalinin var olduğu da unutulmamalıdır. Esasen çalışma, ortaya koyduğu ampirik kanıtlarla, yapay zekâların kendisinin öğrenmeye devam etmesi gerektiğine; özellikle öğrencilerin bireysel özelliklerinin belirlenmesi gibi sıfır tolerans isteyen hususlarda doğrudan yapay zekâ ile hareket edilmesinin riskli olacağına işaret etmektedir.

Makale detayları

ARTIFICIAL INTELLIGENCE IN ACCOUNTING EDUCATION: IDENTIFYING LEARNING STYLES AND ASSESSING INDIVIDUAL DIFFERENCES

Yazarlar:

Fevzi Serkan Özdemir | Haluk Bengü | Eda Turan

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Ekonomi | İşletme ve Yönetim Müfredatı ve Öğretimi

Dergi:

Niğde Ömer Halisdemir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Vol.6 Issue 2, pp.417-436

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336521

pISSN:

2687-5306

Artificial Intelligence

Student-Centered Learning

Personalized Education

Kolb Learning Styles Inventory

Accounting Education

Individual Differences.Yapay Zekâ

Öğrenci Merkezli Öğrenme

Özelleştirilmiş (Yapılandırılmış) Eğitim

Kolb Öğrenme Stilleri Envanteri

Muhasebe Eğitimi

Bireysel Farklılıklar

Sayı

Makale detayları

Yazarlar: Fevzi Serkan Özdemir | Haluk Bengü | Eda Turan

Yayıncı: Nigde Omer Halisdemir University

Ana Başlıklar: Ekonomi | İşletme ve Yönetim Müfredatı ve Öğretimi

Dergi: Niğde Ömer Halisdemir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Vol.6 Issue 2, pp.417-436

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001336521

pISSN: 2687-5306

Sayı

Yazarlar:

Fevzi Serkan Özdemir | Haluk Bengü | Eda Turan

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Ekonomi | İşletme ve Yönetim Müfredatı ve Öğretimi

Dergi:

Niğde Ömer Halisdemir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Vol.6 Issue 2, pp.417-436

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336521

pISSN:

2687-5306