The DOI matches its Crossref record; 15 of the 19 references it cites were found in a registry or an index, and 4 are not checked yet.
Designing Understandable and Fair AI for Learning: The PEARL Framework for Human-Centered Educational AI
Open accessAbstract
As artificial intelligence (AI) is increasingly used in classrooms, tutoring systems, and learning platforms, it is essential that these tools are not only powerful, but also easy to understand, fair, and supportive of real learning. Many current AI systems can generate fluent responses or accurate predictions, yet they often fail to clearly explain their decisions, reflect students’ cultural contexts, or give learners and educators meaningful control. This gap can reduce trust and limit the educational value of AI-supported learning. This paper introduces the PEARL framework, a human-centered approach for designing and evaluating explainable AI in education. PEARL is built around five core principles: Pedagogical Personalization (adapting support to learners’ levels and curriculum goals), Explainability and Engagement (providing clear, motivating explanations in everyday language), Attribution and Accountability (making AI decisions traceable and justifiable), Representation and Reflection (supporting fairness, diversity, and learner self-reflection), and Localized Learner Agency (giving learners control over how AI explains and supports them). Unlike many existing explainability approaches that focus mainly on technical performance, PEARL emphasizes how students, teachers, and administrators experience and make sense of AI decisions. The framework is demonstrated through simulated examples using an AI-based tutoring system, showing how PEARL can improve feedback clarity, support different stakeholder needs, reduce bias, and promote culturally relevant learning. The paper also introduces the PEARL Composite Score, a practical evaluation tool that helps assess how well educational AI systems align with ethical, pedagogical, and human-centered principles. This study includes a small exploratory mixed-methods user study (N = 17) evaluating example AI tutor interactions; no live classroom deployment was conducted. Together, these contributions offer a practical roadmap for building educational AI systems that are not only effective, but also trustworthy, inclusive, and genuinely supportive of human learning.
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“Dakshit et al. (2026) extend this through a human-centered educational AI design framework that places explainability as a core design requirement rather than an optional feature.”
From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Dakshit et al. (2026)
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Atallah, F. A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1887316 · Cited by 5
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XAI-AGLearn: Toward Negotiable Personalization in Explainable Adaptive Gamified Learning Systems
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Self-citationA Competence-Based Architecture for Personalized Adaptive Learning
Jović, J. L., Domazet, D. S., & Zdravković, N. (2026) · Applied Sciences · doi:10.3390/app16209992
The tech-enhanced PEARLS framework: transforming accounting education through digital integration
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Metadata
- Authors
- Dakshit, S., Mokhtari, K., & Khalid, A.
- Affiliations
- Kennesaw State University (US)
- The University of Texas at Tyler (US)
- Corresponding author
- Mokhtari, Kouider
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2026-01-28
- Volume
- 16
- Issue
- 2
- Pages
- 198
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W7125984080
- Cited by
- 4
- References
- 15
- Citations by year
- 2026: 4
- FWCI
- 13.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Education Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- artificial intelligence in education, explainable AI, human-centered design, algorithmic fairness, learner agency, culturally relevant learning, AI tutors, feedback clarity, AI evaluation, trust in AI, bias mitigation
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