Table 2. Results of thematic analysis of... | Emerald Publishing

Table 2.

Results of thematic analysis of AI in education

Dimension	Key insights	Challenges	Data sources	Recommendations
Personalized learning	AI personalizes learning pathways, enhancing engagement, inclusivity and academic outcomes	Privacy concerns, algorithmic bias, digital inequality and high costs	Abbas et al. (2023), Aggarwal (2023), Akavova et al. (2023), Gligorea et al. (2023), Hasibuan and Azizah (2023), Luo and Hsiao-Chin (2023), Neha et al. (2024), Yan et al. (2024)	Develop ethical guidelines, enhance teacher training, implement universal design principles and subsidize access to technology
Ethical and technical issues	Algorithmic biases and data privacy concerns hinder equitable AI adoption and trust	Digital divide, insufficient regulatory frameworks and lack of transparency in algorithms	Al-Zahrani (2024), Abulibdeh et al. (2024), Barnes and Hutson (2024), Sangers et al. (2024), Monserrat et al. (2022), Cotton et al. (2024), Kim (2024)	Establish robust ethical AI frameworks, enhance data security policies and invest in digital infrastructure and diverse data sets
Human–machine collaboration	AI fosters creativity and critical thinking by automating routine tasks and supporting educators	Over-reliance on AI, diminished human agency and lack of teacher readiness	August and Tsaima (2021), Alexsius Pardosi et al. (2024), Cheng and Liang (2023), Moon et al. (2024), Mageira et al. (2022), Nguyen et al. (2024), Ng et al. (2024a)	Balance AI usage with human-led approaches, train educators in AI literacy and design collaborative AI tools for hybrid learning
Policy and teacher training	Effective AI integration depends on trained educators and supportive policy frameworks	Resistance to change, insufficient training resources and funding gaps	Al-Zyoud (2020), Abulibdeh et al. (2024), Chiu and Chai (2020), Rohan Jowallah (2023), Kamir and Diskin (2023), Chiu et al. (2024)	Design scalable training programs focusing on AI ethics and pedagogy, create interdisciplinary policies and foster collaborative networks
Lifelong learning	AI facilitates continuous skill development and lifelong learning through adaptive technologies	Ethical concerns, integration challenges and fear of replacing traditional methods	Alexsius Pardosi et al. (2024), Aggarwal (2023), Tariq et al. (2023), Tran Thi Phuong Nam (2023)	Develop inclusive lifelong learning ecosystems, ensure stakeholder involvement and address ethical implications
Future implications	AI offers opportunities for global collaboration and advancing education	Ethical dilemmas, lack of global standards and potential misuse of data	Cotton et al. (2024), Bond et al. (2024), Bittencourt et al. (2024)	Establish international AI standards, promote ongoing research and foster multistakeholder collaboration

Dimension	Key insights	Challenges	Data sources	Recommendations
Personalized learning	AI personalizes learning pathways, enhancing engagement, inclusivity and academic outcomes	Privacy concerns, algorithmic bias, digital inequality and high costs	Abbas et al. (2023), Aggarwal (2023), Akavova et al. (2023), Gligorea et al. (2023), Hasibuan and Azizah (2023), Luo and Hsiao-Chin (2023), Neha et al. (2024), Yan et al. (2024)	Develop ethical guidelines, enhance teacher training, implement universal design principles and subsidize access to technology
Ethical and technical issues	Algorithmic biases and data privacy concerns hinder equitable AI adoption and trust	Digital divide, insufficient regulatory frameworks and lack of transparency in algorithms	Al-Zahrani (2024), Abulibdeh et al. (2024), Barnes and Hutson (2024), Sangers et al. (2024), Monserrat et al. (2022), Cotton et al. (2024), Kim (2024)	Establish robust ethical AI frameworks, enhance data security policies and invest in digital infrastructure and diverse data sets
Human–machine collaboration	AI fosters creativity and critical thinking by automating routine tasks and supporting educators	Over-reliance on AI, diminished human agency and lack of teacher readiness	August and Tsaima (2021), Alexsius Pardosi et al. (2024), Cheng and Liang (2023), Moon et al. (2024), Mageira et al. (2022), Nguyen et al. (2024), Ng et al. (2024a)	Balance AI usage with human-led approaches, train educators in AI literacy and design collaborative AI tools for hybrid learning
Policy and teacher training	Effective AI integration depends on trained educators and supportive policy frameworks	Resistance to change, insufficient training resources and funding gaps	Al-Zyoud (2020), Abulibdeh et al. (2024), Chiu and Chai (2020), Rohan Jowallah (2023), Kamir and Diskin (2023), Chiu et al. (2024)	Design scalable training programs focusing on AI ethics and pedagogy, create interdisciplinary policies and foster collaborative networks
Lifelong learning	AI facilitates continuous skill development and lifelong learning through adaptive technologies	Ethical concerns, integration challenges and fear of replacing traditional methods	Alexsius Pardosi et al. (2024), Aggarwal (2023), Tariq et al. (2023), Tran Thi Phuong Nam (2023)	Develop inclusive lifelong learning ecosystems, ensure stakeholder involvement and address ethical implications
Future implications	AI offers opportunities for global collaboration and advancing education	Ethical dilemmas, lack of global standards and potential misuse of data	Cotton et al. (2024), Bond et al. (2024), Bittencourt et al. (2024)	Establish international AI standards, promote ongoing research and foster multistakeholder collaboration

Source(s): Authors’ own work