Cited by 4Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning MethodsArtificial Intelligence in Education

“I Am Confused! How to Differentiate Between…?” Adaptive Follow-Up Questions Facilitate Tutor Learning with Effective Time-On-Task

Shahriar, Tasmia, Matsuda, Noboru·2024·Lecture Notes in Computer Science

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Crossref · 10.1007/978-3-031-64299-9_2

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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];”
  • 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].”
  • 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].”
  • 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].”

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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
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
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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