May 21, 2026: Academia and Technology

 

Thoughts over Thoughts

Research and AI Structures

The two readings on produsage provide two central insights. 

1. Education should move towards user-led, collaborative forms of learning that aim to develop skills such as critical thinking, communication, collaboration, creativity, and lifelong learning. 
2. Participation in online culture is not only shaped by users’ interests but also by the structures and logics of digital platforms. Algorithms and interaction formats influence how users create, interpret, and engage with content on platforms like Instagram or YouTube.

Reflecting on the importance of capabilities such as critical thinking in education and digital structures, I began thinking more broadly about research and knowledge production.

AI and Academia

At conferences and universities, the growing number of workshops, presentations, and lectures on AI-related topics demonstrates AI’s increasing relevance and inevitable role in academia. Tools such as ChatGPT have become the subject of intense debate as institutions, faculty, and educators discuss how to integrate them into teaching, writing, and research. Many advocate for a human-first approach that emphasizes human oversight and control, arguing that AI should assist rather than replace human work because it remains prone to mistakes and inaccuracies.

Critical Reflexivity

However, I think control alone is insufficient; deep critical reflexivity is equally important. We must not only question the information AI provides and whether it is “correct,” but also ask why it is presented and how. What kinds of worldviews are embedded in AI-generated responses? What discourses do these systems reinforce or normalize? 

These questions are important as technologies are not neutral. They are trained on datasets that are often grounded in dominant Western epistemologies and cultural assumptions. As a result, they can reproduce stereotypes, marginalize alternative forms of knowledge, and reinforce existing social inequities by perpetuating biased values, perceptions, and beliefs.

Meaning of Research

Beyond reinforcing dominant Western forms of knowledge, AI risks perpetuating neoliberal structures within academia. In fact, scholars face pressures to publish frequently, compete for grants, or produce research aligned with market interests. With vast amounts of online data and AI tools enabling rapid identification, synthesis, and analysis, research increasingly is associated with speed and productivity. This logic reflects broader neoliberal values such as efficiency, competitiveness, and continuous output. This raises a broader question: 

What should research look like?

Conclusion

Overall, technology and AI are transforming how we research and produce knowledge. These developments require critical, ongoing reflection on the information presented by AI, the values and assumptions embedded in it, and the ways we choose to implement technologies in education and academia.