This presentation examines the evolving role of Generative Artificial Intelligence (GAI) in education, focusing on university staff’s expectations, concerns, and strategies for integration. Through the lens of the Sustainable Development Goals (SDGs)—particularly Goal 4: “Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”—the study explores both the challenges and opportunities GAI presents for higher education.

For each of the five key targets of SDG 4, the study identifies potential risks and benefits associated with GAI adoption. Notable strategies proposed by educators include flexible, adaptive regulation, which involves updating policies in response to technological advancements, and distributed responsibility, which acknowledges the transformative nature of GAI and promotes a collective, ecosystem-based approach to its implementation—aligning with the principles of the 2030 Agenda for Sustainable Development.

The Social Construction of Technology (SCOT) framework provides the study’s theoretical foundation, emphasizing that technology adoption is not dictated solely by technical efficiency or individual expertise but is shaped by sociocultural and institutional factors (Bijker et al., 1987). While GAI offers efficiency, personalization, and cost reduction (Pratama et al., 2023; Dittakavi, 2023), it also raises ethical concerns, disrupts traditional educational models, and shifts control over learning processes (Klimova et al., 2023). SCOT views technology as a socially constructed artifact, shaped by user interactions, interpretations, and sector-specific needs (Miao & Chan, 2020). By analyzing how university faculty engage with GAI within their unique institutional and cultural environments, SCOT provides insight into the broader social and organizational dynamics influencing AI adoption in education, highlighting that successful integration depends on more than just technical usability—it requires alignment with evolving social norms, institutional practices, and policy frameworks.

This study employs a phenomenological research approach within the qualitative research tradition (Neuman, 2014). The method was selected to explore and describe university faculty members’ lived experiences regarding the use of GAI in education and the meanings they ascribe to these experiences (van Manen, 1990). This approach enables an in-depth understanding of the challenges, opportunities, and potential pathways for integrating GAI into education, as perceived by faculty members (Creswell, 2013). The study was conducted in Russia, with university faculty members of one university. Purposive sampling was used to ensure the inclusion of participants with specialized knowledge and direct experience in the use of GAI in education (Palinkas et al., 2015). The participants represented various university campuses across Russia and a range of academic disciplines, including economics, history, psychology, computer science, philosophy, and linguistics. They had an average of 15.81 years of academic experience, with seven holding PhDs and four holding Master’s degrees. Data were collected through online semi-structured interviews conducted via Zoom, each lasting 30 to 35 minutes. Each participant was interviewed twice: 1. Exploratory Interview – Gathered initial perspectives on GAI use, associated challenges, opportunities, and future directions in education (Seidman, 2006). 2. Follow-Up Interview – Allowed participants to elaborate on their views and enabled deeper exploration of emerging themes (Merriam & Tisdell, 2016). For data analysis, a hybrid manual-AI approach was used, integrating ChatGPT-3.5 for coding and theme development while maintaining human supervision for validation and refinement. The Braun and Clarke (2006) framework for thematic analysis was applied. The AI-assisted analysis allowed for efficiency in coding and identifying emerging themes while ensuring human oversight to maintain accuracy and contextual integrity (Liew et al., 2014; Longo et al., 2020). This human-in-the-loop approach ensured that AI-generated insights were critically examined and refined by researchers before finalizing results.

While AI has not been specifically designed for education, various solutions, such as chatbots and virtual assistants, are already emerging. Given university staff expectations regarding AI’s continuous improvement, niche AI solutions tailored for education may soon be developed. These could enhance personalization, address concerns about data reliability and ethics, and make learning more accessible—aligning with Target 4.3 (Ensure equal access to post-basic education and training). However, overreliance on AI could hinder the development of critical thinking skills, essential for Target 4.4 (Ensure acquisition of skills relevant for employment). A balanced approach, incorporating AI in context-dependent ways, could help maintain these competencies while leveraging AI’s advantages. Ethical concerns also arise, as AI may overlook cultural contexts, potentially violating global citizenship principles and Target 4.7 (Foster global citizenship through education). Establishing ethical norms and integrating global ethics education into university curricula could mitigate these risks. Additionally, AI-related risks include data unreliability, potential distortions in scientific knowledge, and the psychological stress associated with misapplications. These challenges threaten Target 4a (Ensure a safe and inclusive learning environment). Regulatory frameworks and distributed responsibility models should be developed to ensure AI safety and data protection. University staff also express concerns that AI may replace human educators, diminishing teaching quality and professional significance, which negatively impacts Target 4c (Improve recruitment, training, and working conditions for teachers). To address this, professional development programs should train educators on AI integration, and hybrid models should balance AI and human expertise. Finally, fostering collaboration between educators, students, and AI developers is crucial for adapting AI to education responsibly. By doing so, AI can support, rather than undermine, the foundational principles of higher education.

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