Generative AI chatbot for teachers' data-informed decision-making: Effects and insights

Teachers' instructional decisions rely on effective data collection, analysis, and interpretation. Generative artificial intelligence (AI) offers flexible and efficient support for these processes. However, few studies have explored its practical application in classroom decision-making. This s...

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Published in:Educational Technology ＆ Society Vol. 28; no. 3; pp. 298 - 317
Main Authors: Lee, Jiwon, Lee, Jeongmin
Format: Journal Article
Language:English
Published: International Forum of Educational Technology ＆ Society 01.07.2025
International Forum of Educational Technology & Society, National Taiwan Normal University, Taiwan
International Forum of Educational Technology & Society
Subjects:
Analysis
Data entry
data literacy for teachers
data-informed decision-making
Decision-making
Educational evaluation
generative ai chatbot
Generative artificial intelligence in education: Theories, technologies, and applications
prompt engineering
Teachers
Data-informed decision-making
Generative AI chatbot
Data literacy for teachers
Prompt engineering
ISSN:1176-3647, 1436-4522, 1436-4522
Online Access:Get full text
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Abstract Teachers' instructional decisions rely on effective data collection, analysis, and interpretation. Generative artificial intelligence (AI) offers flexible and efficient support for these processes. However, few studies have explored its practical application in classroom decision-making. This study aimed to develop a generative AI chatbot to enhance teachers' data literacy (DL) and data-informed decision-making (DIDM). Using Richey and Klein's design and development research methodology, this study was conducted in four phases: analysis, design, development, and evaluation. The chatbot was reviewed by five experts and pilot-tested by four participants before being tested in an experiment. A single group pre-post paired samples t-test was conducted with 25 teachers, who interacted with the chatbot on Zoom once a week for 2 weeks. Teachers' reflective journals were examined using open coding procedures to analyze their responses. The findings revealed a significant increase in teachers' DL and DIDM efficacy. A qualitative analysis of the teachers' reflective journals highlighted the chatbot's strengths, limitations, and potential improvements. Based on these findings, this study offers practical implications for developing and using generative AI chatbots to support school teachers' DIDM processes.
AbstractList Teachers' instructional decisions rely on effective data collection, analysis, and interpretation. Generative artificial intelligence (AI) offers flexible and efficient support for these processes. However, few studies have explored its practical application in classroom decision-making. This study aimed to develop a generative AI chatbot to enhance teachers' data literacy (DL) and data-informed decision-making (DIDM). Using Richey and Klein's design and development research methodology, this study was conducted in four phases: analysis, design, development, and evaluation. The chatbot was reviewed by five experts and pilot-tested by four participants before being tested in an experiment. A single group pre- post paired samples t-test was conducted with 25 teachers, who interacted with the chatbot on Zoom once a week for 2 weeks. Teachers ' reflective journals were examined using open coding procedures to analyze their responses. The findings revealed a significant increase in teachers' DL and DIDM efficacy. A qualitative analysis of the teachers' reflective journals highlighted the chatbot's strengths, limitations, and potential improvements. Based on these findings, this study offers practical implications for developing and using generative AI chatbots to support school teachers' DIDM processes.
Teachers' instructional decisions rely on effective data collection, analysis, and interpretation. Generative artificial intelligence (AI) offers flexible and efficient support for these processes. However, few studies have explored its practical application in classroom decision-making. This study aimed to develop a generative AI chatbot to enhance teachers' data literacy (DL) and data-informed decision-making (DIDM). Using Richey and Klein's design and development research methodology, this study was conducted in four phases: analysis, design, development, and evaluation. The chatbot was reviewed by five experts and pilot-tested by four participants before being tested in an experiment. A single group pre- post paired samples t-test was conducted with 25 teachers, who interacted with the chatbot on Zoom once a week for 2 weeks. Teachers ' reflective journals were examined using open coding procedures to analyze their responses. The findings revealed a significant increase in teachers' DL and DIDM efficacy. A qualitative analysis of the teachers' reflective journals highlighted the chatbot's strengths, limitations, and potential improvements. Based on these findings, this study offers practical implications for developing and using generative AI chatbots to support school teachers' DIDM processes. Keywords: Data-informed decision-making, Generative AI chatbot, Data literacy for teachers, Prompt engineering
Audience Academic
Author Jeongmin Lee
Jiwon Lee
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Generative AI chatbot
Data literacy for teachers
Prompt engineering
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Snippet Teachers' instructional decisions rely on effective data collection, analysis, and interpretation. Generative artificial intelligence (AI) offers flexible and...
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SubjectTerms Analysis
Data entry
data literacy for teachers
data-informed decision-making
Decision-making
Educational evaluation
generative ai chatbot
Generative artificial intelligence in education: Theories, technologies, and applications
prompt engineering
Teachers
Title Generative AI chatbot for teachers' data-informed decision-making: Effects and insights
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