Perspectives of secondary school students toward using generative AI as a summative assessment tool of social studies courses in Thailand

Dados Bibliográficos

ID22436664
AutoresSurakrai Nantaburom (0000-0002-0585-6911, Thammasat University, autor correspondente)
Ano2026
Volume5
Fascículo1
Data de publicação2026-05-05
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoDiscover Education (JOURNAL)
Identificadores do periódicoISSN: 2731-5525 • E-ISSN: 2731-5525
EditoraSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s44217-026-01565-z
OpenAlexW7160303582
IdiomaEN
Referências citadas29

Generative artificial intelligence (AI) models, such as ChatGPT, are increasingly used in educational assessment to support explanation, synthesis, and written responses. While such tools offer new possibilities for assessment design, they also raise concerns related to academic integrity, overreliance, accuracy, and fairness, particularly within subject-specific contexts. This study examines Thai secondary students’ perceptions of AI-supported summative assessment using ChatGPT in Social Studies courses. An exploratory case study using a mixed-methods approach was conducted with 121 Grade 10 and Grade 12 students who completed an AI-supported summative writing task under controlled classroom conditions. Data was collected through questionnaires and follow-up semi-structured interviews. Quantitative data were analyzed using descriptive statistics and chi-square tests, indicating generally positive student attitudes toward AI-supported assessment, with no significant differences across gender or grade level. Qualitative analysis constructed three themes and two illustrative insights: enhanced big-picture understanding; support for higher-order thinking; concerns about the accuracy and reliability of AI-generated content; the importance of training and ethical guidance for AI use; and preferences for assessment designs that emphasize analysis, creativity, and personalization rather than rote memorization. Within the Social Studies context, students highlighted illustrative insights suggesting that generative AI is particularly suitable for tasks involving interpretation, explanation, and synthesis, while recognizing its limitations for subjects requiring precise calculation, such as mathematics and physics. These findings provide context-sensitive insights into how generative AI is perceived within Thai summative assessment practices and inform the responsible design of assessments in exam-oriented and digitally uneven educational settings.

Formative assessment · Generative grammar · Perception · Qualitative property · Qualitative research · Social studies · Summative assessment · Thematic analysis · Artificial Intelligence in Healthcare and Education · Computational and Text Analysis Methods · Explainable Artificial Intelligence (XAI