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Cited by 11Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningEducational Technology and Assessment

A closer look at ChatGPT's role in concept map generation for education

Open access
Schicchi, Daniele, Limongelli, Carla, Monteleone, Vito, Taibi, Davide·2026·Interactive Learning Environments
DOI 10.1080/10494820.2025.2497110
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Abstract

Rapid progress in natural language processing (NLP) has ushered in a new era of artificial intelligence (AI) models. Large language models (LLMs), powered by deep learning algorithms, excel in understanding and generating coherent human language responses, revolutionizing various fields. In education, these models promise to enrich the learning experience by providing personalized support to students and facilitating communication between students and teachers. In particular, Generative Pre-trained Transformer (GPT) technology provides instant and contextually relevant responses, and it has become an increasingly interesting topic in education. This article explores the potential of ChatGPT to enhance learning activities in secondary schools, with a specific focus on concept maps, a valuable tool for fostering meaningful learning. Concept maps are widely recognized for their educational benefits but existing tools for automatic concept map generation lack flexibility and semantic understanding. In contrast, ChatGPT's adaptability and responsiveness make it a compelling candidate for generating concept maps tailored to individual needs. This paper sheds light on ChatGPT's potential in education and offers insights into its effectiveness in supporting learning activities through concept map generation. We present a study involving 83 secondary school students to test their perceptions of ChatGPT-generated concept maps. We compared concept maps created by both ChatGPT and teachers across six different topics. Using PlantUML, we standardized the concept maps, and then used various questionnaires to assess their quality, effectiveness, and impact on student performance. The statistical analysis revealed that the concept maps generated by ChatGPT were comparable in quality to those produced by teachers. This highlights the usefulness of ChatGPT in supporting structured learning activities while also significantly reducing the time and effort required to create concept maps.

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  • GenAI takes on the role of co-creator, for example by automatically generating and updating concept maps that students then edit, enabling iterative refinement of external representations within a shared semantic space.

    “Second, GenAI takes on the role of co-creator. For instance, ChatGPT can automatically generate and update concept maps that students then edit, enabling iterative refinement of external representations within a shared semantic space [146].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [146]

  • As a learning partner, AI provides high-quality concept maps, instructional analogies, and personalized feedback, creating deep learning experiences and prompting reflection on socio-ethical implications of technology, thus seeding epistemic insight.

    “As a learning partner, AI provides high-quality concept maps [146], instructional analogies [94], and personalized feedback [128,130], creating deep learning experiences [95] and prompting reflection on socio-ethical implications of technology-thus seeding epistemic insight [117,132,135,138,151].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [146]

  • In a third configuration, AI functions as a materials planner, mass-producing concept maps, leveled texts, or gamified problem sets and predefining reading order and pacing, with classroom control largely ceded to AI after teacher screening.

    “In a third configuration, AI functions as a materials planner. Systems mass-produce concept maps, leveled texts, or gamified problem sets and predefine reading order and pacing; after teachers screen and correct these resources, classroom control is largely ceded to AI.”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [146]

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Authors
Schicchi, D., Limongelli, C., Monteleone, V., & Taibi, D.
Affiliations
  • National Research Council (IT)
  • Roma Tre University (IT)
Corresponding author
Taibi, Davide
ORCID
Venue
Interactive Learning Environments
Publisher
Informa UK Limited
Published
2025-04-30
Volume
34
Issue
1
Pages
276-296
ISSN
  • 1049-4820 (ISSN-L)
  • 1744-5191
DOI
10.1080/10494820.2025.2497110
OpenAlex
W4409967416
Cited by
11
References
60
Citations by year
2026: 9 · 2025: 2
FWCI
11.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Interactive Learning Environments (publishedVersion, cc-by)
  • Iris (Roma Tre University) (submittedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
Keywords
ChatGPT, large language models, high school students, student perceptions, artificial intelligence in education, meaningful learning, PlantUML, student performance

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