Critical and creative pedagogies for artificial intelligence and data literacy: an epistemic data justice approach for academic practice

Javiera Atenas; Leo Havemann; Chrissi Nerantzi

doi:10.25304/rlt.v32.3296

Javiera Atenas School of Social Sciences and Humanities, University of Suffolk, Ipswich, UK https://orcid.org/0000-0001-5006-529X
Leo Havemann Institute of Educational Technology, The Open University, Leeds, UK https://orcid.org/0000-0003-1843-4483
Chrissi Nerantzi Creative and Open Education, Faculty of Social Sciences, School of Education, University of Leeds https://orcid.org/0000-0001-7145-1800

DOI: https://doi.org/10.25304/rlt.v32.3296

Keywords: critical data literacy, data justice, education, creative pedagogies

Abstract

This paper offers guidance on employing open and creative methods for co-designing critical data and artificial intelligence (AI) literacy spaces and learning activities, rooted in the principles of Data Justice. Through innovative approaches, we aim to enhance participation in learning, research and policymaking, fostering a comprehensive understanding of the impact of data and AI whilst promoting inclusivity in critical data and AI literacy. By reflecting on the Higher Education (HE) context, we advocate for active participation and co-creation within data ecosystems, amplifying the voices of educators and learners. Our methodology employs a triangulation model: initially, we conduct interpretative analyses of literature to gauge best practices for curriculum development in HE; then, we examine frameworks in data justice and ethics to identify principles and skills applicable to undergraduate, postgraduate and academic development programs; finally, we explore proposals for critical, creative, ethical, open and innovative ideas for educators to integrate data and AI into their practice.

Downloads

Download data is not yet available.

References

Abegglen, S., Nerantzi, C., Martínez-Arboleda, A., Karatsiori, M., Atenas, J., & Rowell, C. (2024). Towards AI literacy: 101+ creative and critical practices, perspectives and purposes. #creativeHE.

Agarwal, V., Panicker, A., Sharma, A., Rammurthy, R., Ganesh, L., & Chaudhary, S. (2021). Crowdsourcing in higher education: Theory and best practices. In R. Lenart-Gansiniec & J. Chen (Eds.), Crowdfunding in the public sector (pp. 127–135). Springer.

Ahenkorah, E. (2020). Safe and brave spaces don’t work (and what you can do instead). Medium. Retrieved from https://medium.com/@elise.k.ahen/safe-and-brave-spaces-dont-work-and-what-you-can-do-instead-f265aa339aff

Atenas, J., Havemann, L., & Timmermann, C. (2020). Critical literacies for a datafied society: Academic development and curriculum design in higher education. Research in Learning Technology, 28, e2468. https://doi.org/10.25304/rlt.v28.2468

Atenas, J., Havemann, L., & Timmermann, C. (2023). Reframing data ethics in research methods education: A pathway to critical data literacy. International Journal of Educational Technology in Higher Education, 20(1), 11. https://doi.org/10.1186/s41239-023-00380-y

Baack, S. (2015). Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism. Big Data & Society, 2(2), 1–11. https://doi.org/10.1177/2053951715594634

Beghetto, R.A., & Kaufman, J.C. (2014). Classroom contexts for creativity. High Ability Studies, 25(1), 53–69. https://doi.org/10.1080/13598139.2014.905247

Bene, R., & McNeilly, E. (n.d.). Getting radical: Using design thinking to tackle collaboration issues. Papers on Postsecondary Learning and Teaching, 4, 50–57. https://doi.org/10.11575/pplt.v4i.68832

Bhargava, R., Kadouaki, R., Bhargava, E., Castro, G., & D’Ignazio, C. (2016). Data murals: Using the arts to build data literacy. The Journal of Community Informatics, 12(3). Retrieved from https://openjournals.uwaterloo.ca/index.php/JoCI/article/view/3285

boyd, d., & Crawford, K. (2012). Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878

Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A., … Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1). Retrieved from https://asianjde.com/ojs/index.php/AsianJDE/article/view/709

Brand, J., & Sander, I. (2020). Critical data literacy tools for advancing data justice: A guidebook. Data Justice Lab. Retrieved from https://datajustice.files.wordpress.com/2020/06/djl-data-literacy-guidebook.pdf

Brew, M., Taylor, S., Lam, R., Havemann, L., & Nerantzi, C. (2023). Towards developing AI literacy: Three student provocations on AI in higher education. Asian Journal of Distance Education, 18(2), 1–11. https://doi.org/10.5281/zenodo.8032387

British Science Association. (2022). Future Forum: Creativity in STEM: Young people’s views on using collective collaboration to build a better future. British Science Association in collaboration with Unboxed Creativity in the UK. Retrieved from https://www.britishscienceassociation.org/News/future-forum-report-2022-published

Carmi, E., Yates, S. J., Lockley, E., & Pawluczuk, A. (2020). Data citizenship: Rethinking data literacy in the age of disinformation, misinformation, and malinformation. Internet Policy Review, 9(2), 1–22. https://doi.org/10.14763/2020.2.1481

Chaudhry, M.A., & Kazim, E. (2022). Artificial intelligence in education (AIEd): A high-level academic and industry note 2021. AI and Ethics, 2(1), 157–165. https://doi.org/10.1007/s43681-021-00074-z

Couldry, N. (2020). Recovering critique in an age of datafication. New Media & Society, 22(7), 1135–1151. https://doi.org/10.1177/1461444820912536

Couldry, N., & Hepp, A. (2018). The mediated construction of reality. John Wiley & Sons.

D’Ignazio, C., & Bhargava, R. (2015). Approaches to building big data literacy. Proceedings of the Bloomberg Data for Good Exchange Conference. Bloomberg data for good exchange conference. Retrieved from http://www.kanarinka.com/wp-content/uploads/2021/01/DIgnazio-and-Bhargava-Approaches-to-Building-Big-Data-Literacy.pdf

Dalton, C.M., Taylor, L., & Thatcher, J. (2016). Critical data studies: A dialog on data and space. Big Data & Society, 3(1), 1–9. https://doi.org/10.1177/2053951716648346

Danylchenko-Cherniak, O. (2023). Creative and collaborative learning during the Russian-Ukrainian war period: Philological aspects. Philological Treatises, 15(1), 51–61. Retrieved from https://tractatus.sumdu.edu.ua/index.php/journal/article/view/1069

De Almeida, C.P.B., & De Goulart, B.N.G. (2017). How to avoid bias in systematic reviews of observational studies. Revista CEFAC, 19(4), 551–555. https://doi.org/10.1590/1982-021620171941117

Dencik, L., & Sanchez-Monedero, J. (2022). Data justice. Internet Policy Review, 11(1), 1–16. https://doi.org/10.14763/2022.1.1615

Dencik, L., Hintz, A., & Cable, J. (2016). Towards data justice? The ambiguity of anti-surveillance resistance in political activism. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679678

Dencik, L., Hintz, A., Redden, J., & Treré, E. (2019). Exploring data justice: Conceptions, applications and directions. Information, Communication & Society, 22(7), 873–881. https://doi.org/10.1080/1369118X.2019.1606268

D’Ignazio, C. (2022). Creative data literacy: Bridging the gap between the data-haves and data-have nots. Information Design Journal, 23(1), 6–18. https://doi.org/10.1075/idj.23.1.03dig

D’Ignazio, C., & Klein, L. F. (2020). Data feminism. The MIT Press.

Dixon-Woods, M., Cavers, D., Agarwal, S., Annandale, E., Arthur, A., Harvey, J., Hsu, R., Katbamna, S., Olsen, R., Smith, L., Riley, R., & Sutton, A.J (2006). Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Medical Research Methodology, 6(1), 35. https://doi.org/10.1186/1471-2288-6-35

Dubey, M., Otto, J., & Forbes, A. G. (2019). Data brushes: Interactive style transfer for data art (pp. 1–9). IEEE VIS Arts Program (VISAP). Retrieved from https://ieeexplore.ieee.org/document/8900858

Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80–92. https://doi.org/10.1177/160940690600500107

Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society. Mathematical, Physical and Engineering Sciences, 374(2083), 20160360. https://doi.org/10.1098/rsta.2016.0360

Freire, P. (2011). Pedagogy of the oppressed. Continuum.

Gilliard, C. (2017). Pedagogy and the logic of platforms. Educause Review, 52(4). Retrieved from https://er.educause.edu/-/media/files/articles/2017/7/erm174111.pdf

Goel, A.K., & Joyner, D.A. (2017). Using AI to Teach AI: Lessons from an Online AI class. AI Magazine, 38(2), 48–58. https://doi.org/10.1609/aimag.v38i2.2732

Gonsales, P., Buzato, M., & King, E. (2021). Digital literacies and digital inclusion in contemporary Brazil. University of Campinas.

Hamilton, T., & Sharma, S. (1996). Power, power relations, and oppression: A perspective for balancing the power relations. Peace Research, 28(1), 21–41. Retrieved from http://www.jstor.org/stable/23607296

Heeks, R., & Swain, S. (n.d.). An applied data justice framework: Analysing datafication and marginalised communities in cities of the Global South. SSRN Electronic Journal, 74, 1–25. https://doi.org/10.2139/ssrn.3425885

Henz, P. (2021). Ethical and legal responsibility for Artificial Intelligence. Discover Artificial Intelligence, 1(2), 1–5. https://doi.org/10.1007/s44163-021-00002-4

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Retrieved from https://circls.org/primers/artificial-intelligence-in-education-promises-and-implications-for-teaching-and-learning

hooks, b. (1994). Teaching to transgress: Education as the practice of freedom. Routledge.

Jackson, B. (2018). The changing research data landscape and the experiences of ethics review board chairs: Implications for library practice and partnerships. The Journal of Academic Librarianship, 44(5), 603–612. https://doi.org/10.1016/j.acalib.2018.07.001

Jackson, N., Oliver, M., Shaw, M., & Wisdom, J. (2006). Developing creativity in higher education: An imaginative curriculum. Routledge.

Jarke, J., & Breiter, A. (2019). The datafication of education. Learning, Media and Technology, 44(1), 1–6. https://doi.org/10.1080/17439884.2019.1573833

Kara, H., Lemon, N., Mannay, D., & McPherson, M. (2021). Creative research methods in education: Principles and practices. Policy Press.

Komljenovic, J., Williamson, B., Eynon, R., & Davies, H.C. (2023). When public policy ‘fails’ and venture capital ‘saves’ education: Edtech investors as economic and political actors. Globalisation, Societies and Education, 1–16. https://doi.org/10.1080/14767724.2023.2272134

Kordzadeh, N., & Ghasemaghaei, M. (2022). Algorithmic bias: Review, synthesis, and future research directions. European Journal of Information Systems, 31(3), 388–409. https://doi.org/10.1080/0960085X.2021.1927212

Leonard, N. (2021). Emerging artificial intelligence, art and pedagogy: Exploring discussions of creative algorithms and machines for art education. Digital Culture & Education, 13(1), 20–41. Retrieved from https://www.digitalcultureandeducation.com/s/Leonard-2021.pdf

Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018). Fair, transparent, and accountable algorithmic decision-making processes. Philosophy and Technology, 31, 611–627. https://doi.org/10.1007/s13347-017-0279-x

Leurs, K. (2017). Feminist data studies: Using digital methods for ethical, reflexive and situated socio-cultural research. Feminist Review, 115(1), 130–154. https://doi.org/10.1057/s41305-017-0043-1

Llorente, R., & Morant, M. (2015). Crowdsourcing in higher education. In F. Garrigos-Simon, I. Gil-Pechuán, & S. Estelles-Miguel (Eds.), Advances in crowdsourcing (pp. 547–575). Springer. https://doi.org/10.1007/978-3-319-18341-1_7

Lockwood, T. (2010). The bridge between design and business. Design Management Review, 21(3), 5. https://doi.org/10.1111/j.1948-7169.2010.00072.x

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 20, 1–16. https://doi.org/10.1145/3313831.3376727

MacGregor, S., Cooper, A., Searle, M., & Kukkonen, T. (2023) Co-production and arts-informed inquiry as creative power for knowledge mobilisation. Evidence & Policy, 18(2), 206–235. https://doi.org/10.1332/174426421X16478737939339

Manca, A., Atenas, J., Ciociola, C., & Nascimbeni, F. (2017). Critical pedagogy and open data for educating towards social cohesion Pedagogia critica e dati aperti come mezzi per educare alla coesione sociale. Tecnologie Didattiche, 25(1), 111–115. https://doi.org/10.17471/2499-4324/9

Mandinach, E.B., & Gummer, E.S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30–37. https://doi.org/10.3102/0013189X12459803

Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., De Laat, M., Buckingham Shum, S., Gašević, D., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: Artificial Intelligence, 3, 100056. https://doi.org/10.1016/j.caeai.2022.100056

McDougall, R. (2015). Reviewing literature in bioethics research: Increasing rigour in non‑systematic reviews. Bioethics, 29(7), 523–528. https://doi.org/10.1111/bioe.12149

McGovern, N. (2018). Radical collaboration and research data management: An introduction. Research Library Issues, 296, 6–22. https://doi.org/10.29242/rli.296.2

Means, A.J., & Slater, G.B. (2022). Future histories of education, pedagogy, and cultural studies: An editorial introduction. Review of Education, Pedagogy, and Cultural Studies, 44(1), 1–3. https://doi.org/10.1080/10714413.2022.2031750

Milan, S., & Van der Velden, L. (2016). The alternative epistemologies of data activism. Digital Culture & Society, 2(2), 57–74. https://doi.org/10.14361/dcs-2016-0205

Naidu, S. (2021). Building resilience in education systems post-COVID-19. Distance Education, 42(1), 1–4. https://doi.org/10.1080/01587919.2021.1885092

Nerantzi, C., Abegglen, S., Karatsiori, M., & Antonio Martínez-Arboleda (Eds.). (2023). 101 creative ideas to use AI in education, A crowdsourced collection (Version 2023 1.2). Zenodo. https://doi.org/10.5281/ZENODO.8072949

Noble, S.U. (2020). Algorithms of oppression: How search engines reinforce racism. New York University Press.

Nussbaum, B. (2013). Creative Intelligence: Harnessing the power to create, connect, and inspire. Harper Business.

Pangrazio, L., & Selwyn, N. (2019). ‘Personal data literacies’: A critical literacies approach to enhancing understandings of personal digital data. New Media & Society, 21(2), 419–437. https://doi.org/10.1177/1461444818799523

Park, S., & Humphry, J. (2019). Exclusion by design: Intersections of social, digital and data exclusion. Information, Communication & Society, 22(7), 934–953. https://doi.org/10.1080/1369118X.2019.1606266

Perrotta, C. (2022). Advancing data justice in education: Some suggestions towards a deontological framework. Learning, Media and Technology, 48(2), 187–199. https://doi.org/10.1080/17439884.2022.2156536

Prinsloo, P., Slade, S., & Khalil, M. (2022). The answer is (not only) technological: Considering student data privacy in learning analytics. British Journal of Educational Technology, 53(2), 876–893. https://doi.org/10.1111/BJET.13216

Redecker, C., & Punie, Y. (2020). Digital education action plan 2021–2027 resetting education and training for the digital age. Office of the European Union. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1602778451601&uri=CELEX:52020DC0624#footnote32

Schäfer, M.T., & Van Es, K. (Eds.). (2017). The Datafied Society: Studying culture through data. Amsterdam University Press. Retrieved from https://library.oapen.org/handle/20.500.12657/31843

Selwyn, N. (2015). Data entry: Towards the critical study of digital data and education. Learning, Media and Technology, 40(1), 64–82. https://doi.org/10.1080/17439884.2014.921628

Selwyn, N. (2018). Data points: Exploring data-driven reforms of education. British Journal of Sociology of Education, 56(92), 1–9. https://doi.org/10.1080/01425692.2018.1469255

Solemon, B., Ariffin, I., Md Din, M., & Md Anwar, R. (2013). A review of the uses of crowdsourcing in higher education. International Journal of Asian Social Science, 3(9), 2066–2073. Retrieved from https://archive.aessweb.com/index.php/5007/article/view/2564

Stein, S., & De Oliveira Andreotti, V. (2017). Higher education and the modern/colonial global imaginary. Cultural Studies↔ Critical Methodologies, 17(3), 173–181. https://doi.org/10.1177/1532708616672673

Suoranta, J., Teräs, M., Teräs, H., Jandrić, P., Ledger, S., Macgilchrist, F., & Prinsloo, P. (2021). Speculative social science fiction of digitalization in higher education: From what is to what could be. Postdigital Science and Education, 4, 224–236. https://doi.org/10.1007/s42438-021-00260-6

Swist, T., & Gulson, K.N. (2023). Instituting socio-technical education futures: Encounters with/through technical democracy, data justice, and imaginaries. Learning, Media and Technology, 48(2), 181–186. https://doi.org/10.1080/17439884.2023.2205225

Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, December, 1–14. https://doi.org/10.1177/2053951717736335

Taylor, L., & Mukiri-Smith, H. (2021). Human rights, technology and poverty. In M.F. Davis, M. Kjaerum, & A. Lyons (Eds.), Research handbook on human rights and poverty. Edward Elgar Publishing. https://doi.org/10.4337/9781788977517.00049

Thoutenhoofd, E.D. (2017). The datafication of learning: Data technologies as reflection issue in the system of education. Studies in Philosophy and Education, 37, 433–449. https://doi.org/10.1007/s11217-017-9584-1

Treve, M. (2021). What COVID-19 has introduced into education: Challenges facing higher education institutions (HEIs). Higher Education Pedagogies, 6(1), 212–227. https://doi.org/10.1080/23752696.2021.1951616

Webb, C. (1993). Feminist research: Definitions, methodology, methods and evaluation. Journal of Advanced Nursing, 18(3), 416–423. https://doi.org/10.1046/j.1365-2648.1993.18030416.x

Williams, P., & Clarke, B. (2016). Dangerous associations: Joint enterprise, gangs and racism. Centre for Crime and Justice Studies. Retrieved from https://www.crimeandjustice.org.uk/sites/crimeandjustice.org.uk/files/Dangerous%20assocations%20Joint%20Enterprise%20gangs%20and%20racism.pdf

Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice (1st ed.). SAGE Publications.

Williamson, B., Eynon, R., & Potter, J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45(2), 107–114. https://doi.org/10.1080/17439884.2020.1761641

Young, I.M. (2014). Five faces of oppression. Rethinking Power, 174–195. Retrieved from https://www.kenwoodacademy.org/ourpages/auto/2019/9/4/42714114/Five%20Faces%20of%20Oppression.pdf

Zembylas, M. (2023). The analytical potential of ‘affective imaginaries’ in higher education research. Education Inquiry, 1–16. https://doi.org/10.1080/20004508.2023.2223781