AI evidence extraction

Integrating Explainable AI in Education: Challenges and Advancements – A Systematic Review

Zaid M. Altukhi, Sojen Pradhan · Journal of the Association for Information Systems · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

5/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.17705/1pais.18101

Methodology & findings

Study design

Systematic review following PRISMA guidelines with thematic analysis.

Sample

N = 35, 3 groups

Primary method

Thematic analysis to categorise challenges and advancements into seven groups: explainability, ethical, technical, human-computer interaction (HCI), trustworthiness, policy and guideline, and others.

Main result

The study found that "95 challenges and five advancements in the realm of XAI" were identified across the reviewed literature. Notably, "a lack of standardization for performing XAI in educational settings, leading to confusion, particularly regarding ethics, trustworthiness, technicalities, and explainability, which often overlap and vary" was observed. The challenges were "categorised using thematic analysis into seven groups: explainability, ethical, technical, human-computer interaction (HCI), trustworthiness, policy and guideline, and others."

Reports effect sizes.

Research paradigm

Interpretive/qualitative synthesis

Author conclusions

The authors conclude that "This review uncovered the challenges of integrating XAI methods into educational systems. Additionally, through this review, we identified recent advancements developed by researchers in integrating XAI methods into educational AI applications. These advancements utilise new techniques to enhance the trustworthiness of educational AI applications and deliver more accurate results."

Risk of bias

Potential publication bias (only peer-reviewed studies included); Selection bias in database search (major academic databases only, language not specified); Risk of reviewer bias in thematic analysis (no mention of inter-rater reliability or blinding); Potential bias toward English-language publications

Open questions raised

The authors identify that "few articles introduced novel approaches to using XAI in educational settings. This limited number of studies presents both a challenge and an opportunity for the research community to fill this gap with further advancements." The review highlights the need for more research on practical implementations of XAI in education and the development of standardized approaches.
The authors identify that few articles have introduced novel approaches to using XAI in educational settings, presenting an opportunity for the research community to fill this gap with further advancements. They highlight the need for standardization in performing XAI in educational settings.
The authors identify that "few articles introduced novel approaches to using XAI in educational settings" and that "This limited number of studies presents both a challenge and an opportunity for the research community to fill this gap with further advancements" in integrating XAI methods into educational AI applications.

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