“I Am Confused! How to Differentiate Between…?” Adaptive Follow-Up Questions Facilitate Tutor Learning with Effective Time-On-Task
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It also aims to develop students' ability to interpret, discriminate, and critically select among feedback from AI, teachers, and peers.
“(2) develop their ability to interpret, discriminate, and critically select among feedback from AI, teachers, and peers [128-130];”
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 [128]
AI often acts as a dialogic coach or teachable agent that organizes a sequence of demonstration, explanation, questioning, and feedback.
“Beyond answering, AI often acts as a dialogic coach or teachable agent that organizes a sequence of demonstration, explanation, questioning, and feedback, as illustrated by the APLUS mathematics environment and curiosity question training agents [6,102,128].”
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 [128]
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 [128]
These gains tend to depend on structured pedagogical design such as adaptive questioning, error analysis, or project-based learning.
“Notably, these gains tend to depend on structured pedagogical design such as adaptive questioning [127,128], error analysis [77,103], or project-based learning [108].”
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 [128]
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References 22 · Cited by 4
- Authors
- Shahriar, T., & Matsuda, N.
- Affiliations
- North Carolina State University (US)
- Corresponding author
- Matsuda, Noboru
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 17-30
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4400210529
- Cited by
- 4
- References
- 22
- Citations by year
- 2026: 1 · 2025: 3
- FWCI
- 1.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- learning by teaching, large language models, teachable agents, question generation, time on task, problem-solving accuracy, junior high school students, chatbots
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