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Designing for Complementarity: Teacher and Student Needs for Orchestration Support in AI-Enhanced Classrooms
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“Indeed, a proliferation of studies has revealed the emergence of contrasting ethical themes relating to general AI and AIED, most of which are associated with the liability of data across settings, such as in higher education (Zawacki-Richter et al., 2019), K-12 (Holstein et al., 2019), schools (Luckin, 2017), and subjects (Hwang & Tu, 2021).”
From Ethical principles for artificial intelligence in education (2023) · cites this paper as Holstein et al., 2019
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“In addition, there remains limited research into what teachers and students actually want from AIED systems such as requirements around student agency and privacy about which teachers and students might not agree (Holstein et al. 2019).”
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“Indeed, a proliferation of studies has revealed the emergence of contrasting ethical themes relating to general AI and AIED, most of which are associated with the liability of data across settings, such as in higher education (Zawacki-Richter et al., 2019), K-12 (Holstein et al., 2019), schools (Luckin, 2017 ), and su…”
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Metadata
- Authors
- Holstein, K., McLaren, B. M., & Aleven, V.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Holstein, Kenneth
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 157-171
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2952696298
- Cited by
- 189
- References
- 65
- Citations by year
- 2026: 46 · 2025: 45 · 2024: 29 · 2023: 21 · 2022: 21 · 2021: 14 · 2020: 12 · 2019: 1
- FWCI
- 4.59
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- human-AI collaboration, artificial intelligence in education, K-12 education, teacher needs, student needs, AI tutors, human tutoring, student motivation, emotional support, human-centered design
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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