Research Article
Ibrahim Arpaci, Mustafa Baloglu
CONT ED TECHNOLOGY, Volume 18, Issue 3, Article No: ep673
ABSTRACT
This study examined factors that support the sustainable and responsible integration of generative artificial intelligence (GenAI) into higher education. The research model was based on the theory of planned behavior (TPB) and was extended to include ethical considerations, such as authenticity, originality, responsibility, and confidentiality. In the qualitative phase, structured interviews with faculty members across various departments examined the potential benefits, limitations, and ethical concerns of GenAI use in higher education. The qualitative findings informed the development of the theoretical framework and the research model. In the quantitative phase, PLS-SEM was used to test the model with data from 1,261 GenAI users. The results showed that authenticity, originality, responsibility, and confidentiality significantly predicted attitudes (ATs) toward GenAI, while ATs, perceived behavioral control, and subjective norms significantly predicted continuous intention. The findings contribute by testing an ethically grounded extension of the TPB for the responsible integration of GenAI in higher education. They also emphasize the need for clear behavioral rules and ethical guidelines, developed with relevant stakeholders, to support sustainable and responsible use of GenAI.
Keywords: artificial intelligence, AI ethical use, sustainability, mixed methods
Review Article
Alfiya R. Masalimova, Yuliya P. Kosheleva, Aleksandr I. Burov, Olga V. Payushina, Natalia L. Sokolova, Maria A. Khvatova
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep609
ABSTRACT
This study maps 417 peer reviewed publications (2022-2025) at the intersection of sustainability education (SE) and artificial intelligence (AI) using bibliometric methods. We chart venues, co authorship, keyword evolution, and technique usage. The results reveal that “ChatGPT” and “generative AI” are becoming the most popular terms after 2022. Outputs are still mostly from North America and Europe, although contributions from Saudi Arabia, India, and Malaysia are growing. Institutional networks are broken, which means that institutions don’t cooperate together very often. Supervised learning predominates, and neural networks are the most used single technique. We synthesize scattered findings into three practical principles–personalization–protection, competence alignment, and multi-level synchronization–that link AI uses to core SE competencies and support course to institution coordination. The study also shows a dual sustainability lens: AI can help fight climate change, but it also has implications for privacy and the environment. This shows the need for energy reporting and bias safeguards. We suggest causal and longitudinal assessments, collaborative datasets and rubrics, and capacity enhancement for resource-limited environments. Some of the problems are a short citation window (2022-2025), a bias against English speakers, and the possibility of missing databases. Overall, the subject is growing swiftly, but it requires more proof, common standards, and more environmentally friendly ways of doing things to turn AI into lasting educational value.
Keywords: sustainability education, artificial intelligence, generative AI, bibliometric analysis