Our world is experiencing profound and interconnected change. Politically, long-standing global power structures are shifting. Environmentally, communities are increasingly affected by climate-related instability. Economically, many families continue to struggle, and educators often witness these realities when students arrive at school carrying the effects of food insecurity, financial stress, and uncertainty about the future. At the same time, rapid advances in artificial intelligence (AI) are transforming the ways people work, communicate, and learn.
While this picture can seem overwhelming, I remain hopeful. Hope does not mean ignoring challenges; rather, it involves imagining better futures while confronting present realities. Veletsianos et al. (2024) argue that educational technology scholarship should embrace imagination, focus on hopeful futures, and prioritize justice-oriented perspectives. This balance between optimism and realism shapes my vision for educational technology in 2035.
Figure 1 Imagining Educational Technology in 2035
Note. AI-generated image created by the author using Perchance AI Text-to-Image Generator (https://perchance.org/ai-text-to-image-generator).
Future disruptions create opportunities to rethink what matters most in education. If current trends continue, the most valuable educational technologies of 2035 will be those that strengthen human capacities such as critical thinking, ethical reasoning, creativity, collaboration, adaptability, and digital literacy.
In a world increasingly shaped by AI-generated content, algorithmic decision-making, and rapidly evolving digital platforms, digital literacy will become a foundational competency. By 2035, learners will need to critically evaluate sources, recognize bias, understand how algorithms shape information ecosystems, and make ethical decisions about technology use. As Selwyn (2024) argues, educational uses of AI should be grounded in values of trust, care, and shared educational visions, while Roberts (2021) emphasizes the importance of critically examining the assumptions and values embedded within emerging technologies. Educational technology should therefore support learners in becoming informed and responsible digital citizens rather than passive consumers of information.
By 2035, educational technology may be defined by purposeful use, sustainability, personalization, and human-centred learning. Selwyn (2021) suggests that educators should critically evaluate whether technologies meaningfully support learning, rather than adopting tools that offer limited educational value. Educators may progressively focus on identifying technologies that genuinely enhance learning and help students create meaningful opportunities within their lives and communities.
What Happens to the Learner?
The question I keep returning to is what happens to the learner.
In today’s educational system, many students move through a standardized learning pathway, completing courses and assignments that often feel disconnected from their personal interests or future goals. At the same time, many young people seek learning opportunities outside of school through online platforms, communities, and self-directed exploration.
For this reason, I believe learning in 2035 will become more personalized and purposeful. Students may have greater opportunities to pursue projects, inquiries, and learning experiences that connect with their strengths, interests, and aspirations. As AI becomes increasingly capable of supporting differentiated instruction and providing immediate feedback, teachers may be better positioned to support learners as individuals rather than members of a one-size-fits-all system.
While future learning may become more individualized, it should not become more isolated. Czerniewicz and Cronin (2023) describe educational futures in which learning remains deeply connected to community and collective well-being. Future educational technologies should therefore strengthen collaboration, connection, and community engagement rather than simply individualize learning experiences.
Learning will also become increasingly focused on authentic problems. Climate change, economic instability, and social challenges may no longer be abstract concepts for future learners. Instead, they may be realities students experience in their daily lives. As Selwyn (2021) argues, educational systems are likely to face increasing pressures associated with climate-related disasters, migration, food shortages, and broader social and economic destabilization. As a result, schools may move away from isolated academic exercises and toward interdisciplinary investigations that address real-world issues.
This shift reflects broader visions of education that position learning as a collective endeavour rather than an individual pursuit. Czerniewicz and Cronin (2023) describe education as a shared social responsibility that centres people, relationships, and societal well-being. Rather than learning solely for personal achievement, students may increasingly be encouraged to contribute to their communities and engage with authentic challenges.
For example, students in a coastal community might investigate how to preserve declining pink salmon populations. Such a project would require mathematics, environmental science, data collection, ethical reasoning, communication skills, multimedia storytelling, and collaboration with on-site local experts. Rather than learning individual subjects in isolation, students would learn through meaningful engagement with complex community issues.
Figure 2 Human-Centred Learning Futures
Note. AI-generated image created by the author using DeepAI Fantasy World Generator (https://deepai.org/machine-learning-model/fantasy-world-generator).
This vision aligns with futures imagined by Macgilchrist et al. (2020), in which collective action and community engagement contribute to more equitable and sustainable societies. Education becomes less about preparing for the future and more about actively participating in shaping it.
What Happens to the Teacher?
As educational technology evolves, so too will the role of the teacher.
Information is increasingly accessible through search engines, AI tools, digital libraries, and online communities. In this environment, the value of educators lies in helping students interpret, evaluate, and apply information. Anderson and Dron (2011) argue that learning involves learners constructing and confirming new knowledge, suggesting that access to information alone is insufficient without opportunities to make meaning from it.
By 2035, teachers may function less as content providers and more as mentors, facilitators, learning designers, and ethical guides. While AI may assist as a tool for feedback, administrative tasks, and content generation, the uniquely human dimensions of teaching will remain essential. Relationships, empathy, professional judgment, encouragement, and the ability to challenge learners in meaningful ways cannot be automated.
Figure 3 Teachers as mentors in the Future
Note. AI-generated image created by the author using DeepAI Fantasy World Generator (https://deepai.org/machine-learning-model/fantasy-world-generator).
Selwyn (2024) highlights the growing interest in educational AI systems that emphasize human elements of teaching and learning and are rooted in values of trust, care, and shared educational visions. This perspective reinforces the idea that the future of education should not focus on replacing teachers with technology but rather on supporting teachers in the work that matters most. As emerging technologies become progressively integrated into everyday life, learners will require guidance in understanding not only how technology works but also how it shapes society.
What Happens to Assessment?
If learning changes, assessment must change as well.
Current assessment systems often prioritize efficiency, completion, and standardized measures of achievement. While these approaches provide useful information, they do not always capture collaboration, creativity, adaptability, critical thinking, or ethical decision-making. As a result, some students learn to focus on earning grades rather than developing meaningful understanding.
In a future centred on authentic, purposeful learning and community engagement, assessment may increasingly focus on demonstrated competency rather than standardized performance. Portfolios, collaborative projects, community partnerships, and authentic demonstrations of understanding could become steadily significant. Such approaches align with visions of education as a common good, where learning is connected to people, relationships, and meaningful contributions to society (Czerniewicz & Cronin, 2023).
Figure 4 Future of Assessment
Note. AI-generated image created by the author using DeepAI Fantasy World Generator (https://deepai.org/machine-learning-model/fantasy-world-generator).
Rather than asking learners to prove what they know on a single test or assignment, educators may ask students to demonstrate what they can create, contribute, and improve. The emphasis shifts from measuring learning to documenting growth. This approach recognizes learning as an active process of constructing and confirming knowledge rather than simply recalling information (Anderson & Dron, 2011).
This transition reflects a broader shift away from educational systems focused primarily on efficiency and toward systems that value purpose, reflection, and contribution. As Selwyn (2021) suggests, future educational technology may need to move beyond efficiency-driven models and embrace values such as inclusivity, collectivity, reciprocity, and social responsibility.
Conclusion
The future is uncertain. Technology will continue to evolve, and there are legitimate reasons for concern about environmental instability, economic inequality, political polarization, and the growing influence of AI. Yet uncertainty also creates opportunities to rethink the purpose of education.
By 2035, the technologies that matter most may not be those that replace human effort but those that strengthen human capacity. They will support critical thinking, creativity, ethical reasoning, collaboration, community engagement, and digital literacy.
As Selwyn (2021) notes, many critical scholars are motivated by the possibility of creating a better future than the present one. My vision of 2035 embraces this idea. Technology will undoubtedly shape the future of education, but the ultimate goal should remain unchanged: helping learners become thoughtful, ethical, creative, and critically literate citizens capable of contributing to the common good.
Figure 5 The Future of Educational Technology in 2035
Note. AI-generated image created by the author using Perchance AI Text-to-Image Generator (https://perchance.org/ai-text-to-image-generator).
This vision aligns with futures imagined by Czerniewicz and Cronin (2023), in which connected communities learn together, share knowledge collectively, and seek ways for both people and the planet to thrive. In this future, technology serves human flourishing rather than becoming an end in itself. In a rapidly changing world, our greatest educational priority is not developing smarter technologies but nurturing wiser people.
AI Use Statement
Generative AI tools were used during the development of this project to assist with editing and phrasing, revision, and image creation. Final decisions regarding content, interpretation, and written work remained the responsibility of the author.
References
Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. International Review of Research in Open and Distributed Learning, 12(3), 80-97.
Czerniewicz, L., & Cronin, C. (2023). Higher education for good: Teaching and learning futures. Open Book Publishers.
Macgilchrist, F., Allert, H., & Bruch, A. (2020). Students and society in the 2020s. Three future ‘histories’ of education and technology. Learning, Media and Technology, 45(1), 76-89.
Perchance. (2026). AI text-to-image generator [Generative artificial intelligence tool]. https://perchance.org/ai-text-to-image-generator
Roberts, J. (2021). The psychology of artificial intelligence. Routledge.
Selwyn, N. (2021). What’s the problem with learning analytics? Polity Press.
Selwyn, N. (2024). The future of artificial intelligence and education. Routledge.
Veletsianos, G., Kimmons, R., Childs, M., & Roberts, J. (2024). Towards imaginative educational technology scholarship. Learning, Media and Technology.
DeepAI. (2026). Fantasy world generator [Generative artificial intelligence tool]. https://deepai.org/machine-learning-model/fantasy-world-generator