Students’ Use and Perception of Educational GenAI Chatbot in High School Computing: Insights from the Decomposed Theory of Planned Behavior

Generative Artificial Intelligence (GenAI) has gained mass popularity in education due to its versatility and convenience. It is particularly popular amongst younger students, who, as digital natives, are more receptive towards new technology. However, research on students’ attitudes and intentions towards using GenAI technology in their education mainly focuses on older age groups like university students. Utilizing the Decomposed Theory of Planned Behavior (DTPB) as a theoretical framework, this qualitative study interviewed 18 high school computing students to explore their perceptions and intentions to use an educational GenAI chatbot to learn programming and how these decisions may be influenced by individual differences in their academic abilities. Using an abductive approach in analyzing the interview data, the findings align with DTPB to describe students’ attitudes towards using GenAI chatbots in their studies. It also proposes a new construct, students’ perceived need to use GenAI technology, that may influence students’ attitude and intentions. Lastly, the study uncovers differences between students of varying academic abilities in their priorities when using GenAI chatbots that influence their subsequent attitudes. This study extends the user perceptions captured in the existing DTPB framework and suggests how individual factors like academic ability may shape attitudes differently. It also provides recommendations for integrating GenAI into the high school classroom.

Authors

Institutions

Publication Details

Journal
Education Sciences
Published
2025-11-21
DOI
https://doi.org/10.3390/educsci15121569
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Students’ Use and Perception of Educational GenAI Chatbot in High School Computing: Insights from the Decomposed Theory of Planned Behavior

Ling Ling Chan, Ean Teng Khor, Peter Seow
Education Sciences
AI in Service Interactions
article

Students’ Use and Perception of Educational GenAI Chatbot in High School Computing: Insights from the Decomposed Theory of Planned Behavior

Ling Ling Chan, Ean Teng Khor, Peter Seow
article en

Abstract

Generative Artificial Intelligence (GenAI) has gained mass popularity in education due to its versatility and convenience. It is particularly popular amongst younger students, who, as digital natives, are more receptive towards new technology. However, research on students’ attitudes and intentions towards using GenAI technology in their education mainly focuses on older age groups like university students. Utilizing the Decomposed Theory of Planned Behavior (DTPB) as a theoretical framework, this qualitative study interviewed 18 high school computing students to explore their perceptions and intentions to use an educational GenAI chatbot to learn programming and how these decisions may be influenced by individual differences in their academic abilities. Using an abductive approach in analyzing the interview data, the findings align with DTPB to describe students’ attitudes towards using GenAI chatbots in their studies. It also proposes a new construct, students’ perceived need to use GenAI technology, that may influence students’ attitude and intentions. Lastly, the study uncovers differences between students of varying academic abilities in their priorities when using GenAI chatbots that influence their subsequent attitudes. This study extends the user perceptions captured in the existing DTPB framework and suggests how individual factors like academic ability may shape attitudes differently. It also provides recommendations for integrating GenAI into the high school classroom.

Education SciencesVol. 15(12)
Nanyang Technological University (SG)
Nanyang Technological University, Ministry of Education, India, National Institute of Education, Nanyang Technological University
Openalex Percentile: Top 81%
AI in Service Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.