References, Attribution, Resources

Bibliography: 

1. Bachmann, G., Knecht, M., & Wittel, A. (2017). The social productivity of anonymity. Ephemera, 17(2), 241–258.
   
   

2. Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132.
   
   

3. Bayne, S., Knox, J., & Williamson, B. (2020). The datafication of higher education. Learning, Media and Technology, 45(1), 1–5.
   
   

4. Beel, J., & Gipp, B. (2009). Google Scholar’s ranking algorithm: The impact of citation counts. Available on ResearchGate.
   
   

5. Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology.
   
   

6. Bucher, T. (2018). If… Then: Algorithmic Power and Politics. Oxford University Press. (Note: Correct year is 2018, not 2021)
   
   

7. Castañeda, L., & Selwyn, N. (2022). The problem of personalization in digital education. Learning, Media and Technology, 47(2), 158–173.
   
   

8. Cinelli, M., et al. (2021). The echo chamber effect on social media. PNAS, 118(9), e2023301118.
   
   

9. Daniel, J. (2021). The Role of Video-Based Supervision in Education. Education Research Review, 33, 100400.
   
   

10. Edwards, R. (2015). Knowledge infrastructures and the inscrutability of openness in education. Learning, Media and Technology, 40(3), 251–264.
    
    

11. Eynon, R. (2021). The myth of digital personalisation. Learning, Media and Technology, 46(1), 1–12.
    
    

12. Gillespie, T. (2018). Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media. Yale University Press.
    
    

13. Gourlay, L. (2024). Postdigital epistemologies: Knowing, ambiguity and self. Postdigital Science and Education.
    
    

14. Gupta, N., Blair, S., & Nicholas, R. (2020). What we see, what we don’t see. Journal of Field Archaeology, 45(sup1), S39–S50.
    
    

15. Knox, J., Williamson, B., & Bayne, S. (2020). Machine behaviourism: Future visions of ‘learning analytics’. Learning, Media and Technology, 45(1), 31–45.
    
    

16. Laakso, M., Matthias, L., & Jahn, N. (2021). Open Access is Not Forever: A Study of Vanished Open Access Journals. arXiv preprint.
    
    

17. Luckin, R. (2021). AI in Education: Promises and Pitfalls. Educational Technology Research and Development, 69(2), 435–452.
    
    

18. Napoli, P. M. (2019). Social Media and the Public Interest: Media Regulation in the Disinformation Age. Columbia University Press.
    
    

19. Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
    
    

20. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing.
    
    

21. Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
    
    

22. Ross, J., Sinclair, C., & Knox, J. (2021). Digital Education Futures: Designing for Doing Education Differently. Irish Educational Studies, 40(2), 191–205.
    
    

23. Shoemaker, P. J., & Vos, T. (2009). Gatekeeping Theory. Routledge.
    
    

24. UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. Link to official document
    
    

25. Williamson, B. (2017). Big Data in Education: The Digital Future of Learning, Policy and Practice. SAGE.
    
    

26. Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.
    
    

27. Youvan, D. C. (2025). The Future of Knowledge: AI as a Living Publication and the Evolution of Research Dissemination.

Open Educational Resources 

1. 1. OER Commons: AI Resources for Educators
      A growing community and curated collection of open-access resources exploring AI's role in education. Includes lesson plans, ethics modules, and project-based activities.

      • Bytes of AI: AI & Ethics (CC BY-NC-SA 4.0)
        
        

      • Exploring the Challenges of AI in Education (CC BY-NC-SA 4.0)
        
        

      • Data Science & AI in Psychology (CC BY-NC-SA 4.0)
        
        

      • Hands-On AI Ethics Projects (CC BY-NC-SA 4.0)
        
        

   2. The Turing Way
      An open-source guide to reproducible, ethical, and inclusive data science. (CC BY 4.0)​ 

   3. Creating Open Educational Resources – The Open University
      A beginner-friendly course for educators interested in making their own open resources. Includes licensing tips, content curation, and digital pedagogy guidance. (CC BY-NC-SA 4.0)​​

Image Credit

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