Generative AI in education and research: A systematic mapping review

Review of Education, Год журнала: 2024, Номер 12(2)

Опубликована: Авг. 1, 2024

Abstract Given the potential applications of generative AI (GenAI) in education and its rising interest research, this systematic review mapped thematic landscape 407 publications indexed Web Science, ScienceDirect Scopus. Using EPPI Reviewer, publication type, educational level, disciplines, research areas GenAI were extracted. Eight discursive themes identified, predominantly focused on ‘application, impact potential’, ‘ethical implication risks’, ‘perspectives experiences’, ‘institutional individual adoption’, ‘performance intelligence’. was conceptualised as a tool for ‘pedagogical enhancement’, ‘specialised training practices’, ‘writing assistance productivity’, ‘professional skills development’, an ‘interdisciplinary learning tool’. Key gaps highlighted include paucity discussions K‐12 education; limited exploration GenAI's using experimental procedures; ethical concerns from lens cultural dimensions. Promising opportunities future are highlighted.

Язык: Английский

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Implementing a proposed framework for enhancing critical thinking skills in synthesizing AI-generated texts

Thinking Skills and Creativity, Год журнала: 2024, Номер 53, С. 101619 - 101619

Опубликована: Авг. 8, 2024

Язык: Английский

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A comprehensive analysis of AI adoption, implementation strategies, and challenges in higher education across the Middle East and North Africa (MENA) region

Education and Information Technologies, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

Язык: Английский

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Investigating student engagement with AI-driven feedback in translation revision: A mixed-methods study

Education and Information Technologies, Год журнала: 2025, Номер unknown

Опубликована: Март 5, 2025

Язык: Английский

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GenAI Learning for Game Design: Both Prior Self-Transcendent Pursuit and Material Desire Contribute to a Positive Experience

Big Data and Cognitive Computing, Год журнала: 2025, Номер 9(4), С. 78 - 78

Опубликована: Март 27, 2025

This study explores factors influencing positive experiences with generative AI (GenAI) in a learning game design context. Using sample of 26 master’s-level students course on AI’s societal aspects, this examines the impact (1) prior knowledge and attitudes toward technology learning, (2) personal value orientations. Results indicated that both students’ self-transcendent goals desire for material benefits have correlations collaborative, cognitive, affective outcomes. However, are stronger predictor, as determined by stepwise regression analysis. Attitudes were positively associated cognitive outcomes during first week, though association did not persist into second week. Most other attitudinal variables collaborative or but linked to negative affect. These findings suggest values correlate more strongly aspects using GenAI educational than their attributes. result may indicate experience neutralizes effect earlier towards technology, major influences deriving from If these borne out, has implications utility current efforts change especially those encourage women STEM topics. Thus, it be that, rather pro-technology instruction, focus orientations would effective way diverse participate programs.

Язык: Английский

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From text to moving image: Evaluating generative artificial intelligence text-to-video models for pre-writing idea generation in language instruction

Education and Information Technologies, Год журнала: 2025, Номер unknown

Опубликована: Апрель 3, 2025

Язык: Английский

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Pre-service Mathematics Teachers’ and Engineering Students’ Perceptions of ChatGPT in Mathematics: Development, Validation And Implementation Study

Digital Experiences in Mathematics Education, Год журнала: 2025, Номер unknown

Опубликована: Март 29, 2025

Язык: Английский

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GenAI Tools in Academic Reading: A Study on AI-Assisted Metacognitive Strategies and Emotional Reactions

Lecture notes in educational technology, Год журнала: 2025, Номер unknown, С. 98 - 112

Опубликована: Янв. 1, 2025

Язык: Английский

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Navigating Ethical Frameworks to Mitigate Academic Misconduct While Leveraging Generative AI

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 21, 2024

The rapid advancement of Generative AI in academia raises ethical concerns about academic integrity. This study aims to delineate the key prevalent and propose a theoretical framework that incorporates deontological ethics for learners teleological evaluators. Employing qualitative methodology thematic analysis, this research undertakes systematic scoping review scholarly articles. researcher searched various databases, following specific inclusion exclusion criteria, he selected final set 68 relevant studies out 200 review. found lack integrity, particularly written assignments, due heightened risk plagiarism, address them, establishment guidelines was effective learners' awareness using inspiring educators assess learners’ creation emphasizing own creativity. has potential inform development use academia. As generative tools become increasingly prevalent, misconduct escalates, thereby threatening educational institutions' credibility qualifications' will help understand how frameworks can mitigate plagiarism foster culture among students educators.

Язык: Английский

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Rejection or integration of AI in academia: determining the best choice through the Opportunity Cost theoretical formula

Discover Education, Год журнала: 2024, Номер 3(1)

Опубликована: Ноя. 25, 2024

The abrupt evolution of Artificial Intelligence (AI) in academia has spurred a complex debate regarding its rejection or integration academia. This study aims to portray comparative analysis the risks associated with AI and missed opportunities absence academic settings. Utilizing economic theory Opportunity Cost as theoretical framework, investigates whether potential gains from adoption outweigh losses. is fundamental principle economics, which determines best alternative between two choices single context, guiding individuals organizations make choice. Adopting qualitative methodology for this systematic review, research employs content analysis. Using Boolean formula, researcher constructed precise search queries retrieve relevant literature across six databases applied specific protocols inclusion exclusion; an initial pool 260 existing literature, 72 studies were selected based on bibliometrics final synthesis avoid fallacy composition, wrong decision about AI. findings indicate that blessings generative significantly risks, leading integrate Although recorded negative aspects, these are not substantial enough undermine overall positive impact AI, it holds considerable promise fostering dynamic environments. inform shape user attitudes toward provides valuable insights institutions, educators, policymakers.

Язык: Английский

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