Human-in-the-loop assessment with AI: implications for teacher education in Ibero-American universities

This scoping review examines how artificial intelligence (AI) reshapes assessment in Ibero-American higher education and specifies the teacher-training capacities and ethical safeguards needed for responsible adoption. Guided by PRISMA procedures and an eligibility scheme based on PPCDO (Population–Phenomenon–Context–Design–Outcomes), we searched Scopus and screened records (2015–2025; English/Spanish/Portuguese), yielding 76 peer-reviewed studies. Synthesis combined qualitative thematic analysis with quantitative descriptors and an exploratory correlational look at tool–outcome pairings. Rather than listing generic ICT, we propose a function-by-purpose taxonomy of assessment technologies that distinguishes pre-AI baselines from AI-specific mechanisms–generativity, adaptivity, and algorithmic feedback/analytics. Read through this lens, AI’s value emerges when benefits are paired with conditions of use: explainability practices, data stewardship, audit trails, and clearly communicated assistance limits. The review translates these insights into a decision-oriented agenda for teacher education, specifying five competency clusters: (1) feedback literacy with AI (criterion-anchored prompting, sampling and audits, revision-based workflows); (2) rubric/item validation and traceability; (3) data interpretation and fairness; (4) integrity and transparency in AI-involved assessment; and (5) orchestration of platforms and moderation/double-marking when AI assists scoring. Exploratory correlations reinforce these priorities, signaling where training should concentrate. We conclude that Ibero-American systems are technically ready yet pedagogically under-specified: progress depends less on adding tools and more on professionalizing human-in-the-loop assessment within robust governance. The article offers a replicable taxonomy, actionable training targets, and a research agenda on enabling conditions for trustworthy, AI-enhanced assessment.

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Publication Details

Journal
Frontiers in Education
Published
2025-10-23
DOI
https://doi.org/10.3389/feduc.2025.1710992
Citations
4
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
6.77

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article

Human-in-the-loop assessment with AI: implications for teacher education in Ibero-American universities

Javier Mella-Norambuena, Andrés Chiappe, Diana Carolina Fajardo-Ramos
4 citations
Frontiers in Education
Online Learning and Analytics
6.77
article

Human-in-the-loop assessment with AI: implications for teacher education in Ibero-American universities

Javier Mella-Norambuena, Andrés Chiappe, Diana Carolina Fajardo-Ramos
article en
4 citations

Abstract

This scoping review examines how artificial intelligence (AI) reshapes assessment in Ibero-American higher education and specifies the teacher-training capacities and ethical safeguards needed for responsible adoption. Guided by PRISMA procedures and an eligibility scheme based on PPCDO (Population–Phenomenon–Context–Design–Outcomes), we searched Scopus and screened records (2015–2025; English/Spanish/Portuguese), yielding 76 peer-reviewed studies. Synthesis combined qualitative thematic analysis with quantitative descriptors and an exploratory correlational look at tool–outcome pairings. Rather than listing generic ICT, we propose a function-by-purpose taxonomy of assessment technologies that distinguishes pre-AI baselines from AI-specific mechanisms–generativity, adaptivity, and algorithmic feedback/analytics. Read through this lens, AI’s value emerges when benefits are paired with conditions of use: explainability practices, data stewardship, audit trails, and clearly communicated assistance limits. The review translates these insights into a decision-oriented agenda for teacher education, specifying five competency clusters: (1) feedback literacy with AI (criterion-anchored prompting, sampling and audits, revision-based workflows); (2) rubric/item validation and traceability; (3) data interpretation and fairness; (4) integrity and transparency in AI-involved assessment; and (5) orchestration of platforms and moderation/double-marking when AI assists scoring. Exploratory correlations reinforce these priorities, signaling where training should concentrate. We conclude that Ibero-American systems are technically ready yet pedagogically under-specified: progress depends less on adding tools and more on professionalizing human-in-the-loop assessment within robust governance. The article offers a replicable taxonomy, actionable training targets, and a research agenda on enabling conditions for trustworthy, AI-enhanced assessment.

Frontiers in EducationVol. 10
Universidad de La Sabana (CO), University of the Americas (CL)
Universidad de La Sabana
Openalex Percentile: Top 3%
Online Learning and Analytics
6.77
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