From principles to practice: a novel matrix for evaluating AI-powered learning platforms based on the UNESCO Ethical Impact Assessment tool - Isaifan, Rima J.; Tawalbeh, Ayman; Hasna, Mazen O. (Frontiers in Education, 2025)
IntroductionArtificial intelligence (AI) is reshaping education by enabling adaptive learning, personalized feedback, and data-driven decision support. However, systematic tools to evaluate the ethical and pedagogical readiness of AI educational platforms remain limited, particularly in culturally specific contexts. This study addresses this gap by operationalizing the UNESCO Ethical Impact Assessment (EIA) Tool through the development of the Gulf-AI Education Tool Evaluation Matrix (G-AIETM), a structured framework designed to assess AI-powered educational platforms against 18 ethical and pedagogical indicators.MethodsThe G-AIETM framework was applied to evaluate seven globally recognized AI-powered educational platforms: Khanmigo, CENTURY Tech, MATHia, Knewton Alta, AltSchool, Querium, and Squirrel AI. Each platform was assessed against 18 criteria using a 5-point Likert scale, and normalized scores were calculated to generate rankings out of 100. The study further developed an actionable implementation framework specifically for Khanmigo, which included phases such as Arabic natural language integration, curriculum adaptation, ethical AI training for educators, and localized data hosting. Enabling factors for the application of the UNESCO EIA tool–such as cross-disciplinary stakeholder engagement and iterative use–were also identified, alongside persistent barriers including resource limitations and regulatory gaps.ResultsThe evaluation revealed that Khanmigo achieved the highest score of 74.4%, qualifying as “Recommended with Minor Adaptation,” due to strengths in adaptive learning, stakeholder dashboards, and ethical integration. However, it was limited by insufficient Arabic language support and local compliance mechanisms. CENTURY Tech and MATHia each scored 67.8%, showing solid technical performance but requiring significant localization in language, curriculum alignment, and data governance. Knewton Alta (58.9%), AltSchool (57.8%), Querium (54.4%), and Squirrel AI (52.2%) were categorized as “Needs Significant Localization,” reflecting deficits in Arabic support, cultural sensitivity, and transparency in algorithmic processes and data privacy.DiscussionThe findings underscore the urgent need for cultural and linguistic localization in AI for education. A critical issue across platforms was the persistent lack of Arabic language integration and Islamic cultural alignment, raising concerns about inclusivity and trust in AI outputs within Gulf classrooms. These limitations highlight the ethical imperative of ensuring context-specific adaptation before large-scale deployment. By grounding the G-AIETM in contemporary theories of responsible innovation and ethics-by-design, the study extends beyond descriptive evaluation to provide a replicable, evidence-based model for policymakers, educators, and developers. This contributes novel insights into the ethical governance of AI in education by combining a globally recognized assessment tool with a culturally responsive matrix, offering practical policy implications for Qatar and comparable education systems worldwide.