ReviewAI and ethics2022
Artificial intelligence in education: Addressing ethical challenges in K-12 settings.
Review in AI and ethics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 1,091 citations in OpenAlex.
- Ethical issues of the use of AI-driven mobile apps for education.Frontiers in public health · 2022Pooled it
- AI-assisted case-based learning and flipped classroom to improve clinical decision-making: a randomized controlled trial in reproductive medicine.Medical education online · 2026Trial
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- A Double-Edged Algorithm Attitude: How Appreciation and Aversion Shape Students' AI Learning Anxiety in Higher Education.Behavioral sciences (Basel, Switzerland) · 2026Article
- A machine learning framework with a public synthetic data set for early student profiling and outcome prediction.Scientific reports · 2026Article
- Faculty perceptions of AI for personalized instruction and learning facilitation in higher education.Scientific reports · 2026Article
- Integration of artificial intelligence competencies into a holistic digital literacy framework for middle school education.Scientific reports · 2026Article
- Development and validation of an AI use scale for sport and exercise science students.Scientific reports · 2026Article
- Development, Network Analysis, and Validation of the Ethical-Spiritual Algorithmic Trust Calibration Scale (ES-ATCS): Exploring Teachers' Ethical and Spiritual Trust in AI Integration Within Jordanian Secondary Education.Journal of religion and health · 2026Article
- Article
- Editorial: Research ethics and integrity in the artificial intelligence era.Frontiers in research metrics and analytics · 2026Article
- AI and higher education: teachers' perceptions of students' ethical tensions in online pedagogical methods.Frontiers in psychology · 2026Article
- The first scene of artificial intelligence curriculum reform: longitudinal evidence on teachers' stress and multidimensional anxiety.Frontiers in psychology · 2026Article
- The mechanisms influencing AI teaching readiness among preservice STEM teachers: an empirical analysis based on PLS-SEM and fsQCA.Frontiers in psychology · 2026Article
- Student perceptions of artificial intelligence in higher education: a structural analysis at an Italian university.Frontiers in psychology · 2026Article
- AI literacy and high school English teachers' teaching anxiety: the chain mediation role of teaching self-efficacy and work engagement.Frontiers in psychology · 2026Article
- AI and the future of teachers: predicting stakeholders' expectations of teacher job loss from AI-related institutional conditions.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence in college students' mental health education: opportunities, challenges, and strategic responses.Frontiers in psychology · 2026Review
- Open Auditorium-A human-centric extended reality blended learning tool.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence and the transformation of education: a systematic narrative review towards adaptive epistemic ecosystems and post-linear pedagogy.Frontiers in artificial intelligence · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. Applications of AI transform the tools of education. AI has a variety of educational applications, such as personalized learning platforms to promote students' learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners' behaviors. Despite the potential benefits of AI to support students' learning experiences and teachers' practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts. The ethical challenges of AI in education must be identified and introduced to teachers and students. To address these issues, this paper (1) briefly defines AI through the concepts of machine learning and algorithms; (2) introduces applications of AI in educational settings and benefits of AI systems to support students' learning processes; (3) describes ethical challenges and dilemmas of using AI in education; and (4) addresses the teaching and understanding of AI by providing recommended instructional resources from two providers-i.e., the Massachusetts Institute of Technology's (MIT) Media Lab and Code.org. The article aims to help practitioners reap the benefits and navigate ethical challenges of integrating AI in K-12 classrooms, while also introducing instructional resources that teachers can use to advance K-12 students' understanding of AI and ethics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.