Abstract
Over the past two decades, innovations powered by artificial intelligence (AI) have extended into nearly all facets of human experience. Our ethnographic research suggests that while young people sense they can't “trust” AI, many are not sure how it works or how much control they have over its growing role in their lives. In this study, we attempt to answer the following questions: (1) What can we learn about young people's understanding of AI when they produce media with and about it? and (2) What are the design features of an ethics-centered pedagogy that promotes STEM engagement via AI? To answer these questions, we co-developed and documented three projects at YR Media, a national network of youth journalists and artists who create multimedia for public distribution. Participants are predominantly youth of color and those contending with economic and other barriers to full participation in STEM fields. Findings showed that by creating a learning ecology that centered the cultures and experiences of its learners while leveraging familiar tools for critical analysis, youth deepened their understanding of AI. Our study also showed that providing opportunities for youth to produce ethics-centered interactive stories interrogating invisibilized AI functionalities, and to release those stories to the public, empowered them to creatively express their understandings and apprehensions about AI.
- [1] . 2016. None but ourselves can free our minds: Critical computational literacy as a pedagogy of resistance. Equity & Excellence in Education 49, 4 (2016), 480–492.
DOI: Google ScholarCross Ref - [2] . 2019. What if you ruled the school dress code. Retrieved from www.yr.media/interactive/what-if-you-ruled-the-school-dress-code/.Google Scholar
- [3] . 2020. Retrieved from www.interactive.yr.media/coronavirus-comes-to-campus/.Google Scholar
- [4] . 2018. Beyond coding: Using critical computational literacy to transform tech. Texas Education Review 6, 1 (2018), 10–16. Available at http://hdl.handle.net/2152/64975.Google Scholar
- [5] . 2006. From the achievement gap to the education debt: Understanding achievement in U.S. schools. Educational Researcher 35, 7 (2006), 3–12.
DOI: Google ScholarCross Ref - [6] . 1986. Teachers and machines: The classroom use of technology since 1920.
DOI: Google ScholarCross Ref - [7] . 2012. Answering the big question on new technology in schools: Does it work? (Part 1). Retrieved from http://larrycuban.wordpress.com/2012/03/10/answering-the-big-question-on-new-technology-in-schools-does-it-work-part-1/.Google Scholar
- [8] . 2014. Using technology to support at-risk students’ learning. Alliance for Excellent Education and Stanford Center for Opportunity Policy in Education. Retrieved from https://edpolicy.stanford.edu/sites/default/files/scope-pub-using-technology-report.pdf.Google Scholar
- [9] . 2013. L.A. students breach school ipads’ sSecurity. Los Angeles Times. 2013. Retrieved from http://articles.latimes.com/2013/sep/24/local/la-me-lausd-ipads-20130925.Google Scholar
- [10] . 2019. Introduction to solidarities of nonalignment: Abolition, decolonization, and anticapitalism. Critical Ethnic Studies 5, (1–2), 5–20.
DOI: Google ScholarCross Ref - [11] . 2005. Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education 8, 1 (2005), 69–91.Google ScholarCross Ref
- [12] A. Calabrese-Barton and M. Tan. 2020. “Beyond equity as inclusion: A framework of “rightful presence” for guiding justice-oriented studies in teaching and learning.” 49, 6 (2020), 433–440.Google Scholar
- [13] . 2003. Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher 32, 5 (2003), 19–25.
DOI: Google ScholarCross Ref - [14] . 2014. The presence of culture in learning. In Handbook of Research on Educational Communications and Technology. Springer, New York, NY, 349–361.
DOI: Google ScholarCross Ref - [15] . 2001. The laboratory of comparative human cognition. Handbook of Research on Teaching (2001), 951–997.Google Scholar
- [16] . 2009. Cultural-historical approaches to literacy teaching and learning. Breaking the Silence: Recognizing the Social and Cultural Resources Students Bring to the Classroom: 60–77.Google Scholar
- [17] . 2018. Avoiding educational technology pitfalls for inclusion and equity. TechTrends, 1–9.Google Scholar
- [18] . 2018. Ethics, identity, and political vision: Toward a justice-centered approach to equity in computer science education. Harvard Educational Review 88, 1 (2018), 26–52.Google ScholarCross Ref
- [19] . 2020. Equity-centered approaches to educational technology. Handbook of Research in Educational Communications and Technology (2020), 247–261. Available at .Google ScholarCross Ref
- [20] . 2013. Toward culturally responsive computing education. Communications of the ACM 56, 7 (2013), 33–36.Google ScholarDigital Library
- [21] . 2013. Critical ancestral computing: A culturally relevant computer science education. PsychNology Journal 11, 1 (2013).Google Scholar
- [22] . 2009. The use and misuse of computers in education: Evidence from a randomized controlled trial of a language arts program. Cambridge, MA: Abdul Latif Jameel Poverty Action Lab (JPAL).Google Scholar
- [23] . 2015. One laptop per child at home: Short-term impacts from a randomized experiment in Peru. American Economic Journal: Applied Economics 7, 2 (2015), 53–80.Google ScholarCross Ref
- [24] . 2012. Technology and child development: Evidence from the one laptop per child program. SSRN Electronic Journal.Google ScholarCross Ref
- [25] . 2016. Does technology improve reading outcomes? Comparing the effectiveness and cost-effectiveness of ICT interventions for early grade reading in Kenya. International Journal of Educational Development 49, 204–214.Google ScholarCross Ref
- [26] . 2018. Artificial Intelligence and the Future of Humans. Washington, DC: Pew Research Center.Google Scholar
- [27] . 2016. The emerging role of artificial intelligence in modern society. International Journal of Creative Research Thoughts 4, 4 (2016), 906–911.Google Scholar
- [28] 2020. AI for social good: Unlocking the opportunity for positive impact. Nat. Commun. 11, 2468 (2020). Google ScholarCross Ref
- [29] . 2019. Artificial intelligence in society, OECD Ppublishing, Pparis, Ffrance. Retrieved from Google ScholarCross Ref
- [30] . 2018. The Future Computed: Artificial Intelligence and its Role in Society. Redmond, Washington: Microsoft Corporation.Google Scholar
- [31] . 2019. Race After Technology: Shining Light on the New Jim Code. Cambridge, UK: Polity.Google Scholar
- [32] . 2016. Why we should expect algorithms to be biased. MIT Technology Review. Retrieved from https://www.technologyreview.com/s/601775/why-we-should-expect-algorithms-to-be-biased/.Google Scholar
- [33] . 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York, NYU Press.Google ScholarCross Ref
- [34] . 2016. The national artificial intelligence research and development strategic plan. Retrieved from https://www.nitrd.gov/pubs/national_ai_rd_strategic_plan.pdf.Google Scholar
- [35] . 2015. I always assumed that I wasn't really that close to [her]: Reasoning about invisible algorithms in news feeds. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 153–162.Google ScholarDigital Library
- [36] . 2019. Minors and artificial intelligence – implications to media literacy. Information Technology and Systems: Proceeding of ICITS 2019. 881–890. Google ScholarCross Ref
- [37] . 2016. How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society 3, 1, 2053951715622512.Google ScholarCross Ref
- [38] . 2019. Can children understand machine learning concepts? The effect of uncovering black boxes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–11.Google ScholarDigital Library
- [39] . 2014. Algorithmic-accountability: The investigation of black boxes. Tow Center for Digital Journalism, Columbia University. Google ScholarCross Ref
- [40] . 2017. European Uunion regulations on algorithmic decision-making and a “right to explanation”. AI Mmagazine 38, 3 (2017), 50–57.Google ScholarCross Ref
- [41] . 2020. What is AI lLiteracy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–16. Google ScholarDigital Library
- [42] . 2018. How smart are the smart toys? Children and parents' agent interaction and intelligence attribution. In Proceedings of the 17th ACM Conference on Interaction Design and Children. 231–240.Google ScholarDigital Library
- [43] . 2020. From algorithmic surveillance to algorithmic awareness: Media education in the context of new media economics and invisible technologies. In Media Education as a Challenge. Warsaw: Academy of Fine Arts in Warsaw Publishing. , & (Eds.). Retrieved from https://www.academia.edu/43171356/From_Algorithmic_Surveillance_to_Algorithmic_Awareness_Media_Education_in_the_Context_of_New_Media_Economics_and_Invisible_Technologies_Democratisation_of_the_web_and_the_beginnings_of_data_driven_capitalism.Google Scholar
- [44] . 2020. https://ai-4-all.org/.Google Scholar
- [45] . 2020. Learning about artificial intelligence: A hub of MIT resources for K-12 students. Retrieved from https://www.media.mit.edu/articles/learning-about-artificial-intelligence-a-hub-of-mit-resources-for-k-12-students/.Google Scholar
- [46] . 2019. Envisioning AI for K-12: What should every child know about AI? Proceedings from the Tthirty-third AAAI Cconference on Aartificial Iintelligence. Association for the Advancement of Artificial Intelligence.Google ScholarDigital Library
- [47] . 2019. Media Education 3.0? How Big Data, Algorithms, and AI Redefine Media Education. The Handbook on Media Education Research, Divina Frau Meigs, Sirkku Kotilainen, Manisha Pathak-Shelat, Michael Hoechsmann, and Stuart R. Poyntz (Eds.). New York: Wiley- Blackwell (International Academic Media Studies and Communication Global Handbooks in Media and Communication Research) [PDF] ttu.edu.Google Scholar
- [48] . 2020. Learning machine learning with personal data helps stakeholders ground advocacy arguments in model mechanics. ACM International Computing Education Research Conference (ICER). 67–78. Google ScholarDigital Library
- [49] . 1996. Pedagogy of the oppressed (revised). New York: Continuum.Google Scholar
- [50] . 2006. Computational thinking. Communications of the ACM 49, 3 (2006), 33–35.Google ScholarDigital Library
- [51] . 2021. The trouble with STEAM and why we use it anyway. Science Education 105, 2 (2021), 209–231.Google ScholarCross Ref
- [52] . 2005. Creative code. Education 7, 177.Google Scholar
- [53] . 2005. Creative coding: Programming for personal expression.Google Scholar
- [54] . 2019. Applying conservation ethics to the examination and treatment of software-and computer-based art. Journal of the American Institute for Conservation 58, 3 (2019), 180–195.Google ScholarCross Ref
- [55] . 2016. Interrupting everyday life: Public interventionist art as critical public pedagogy. International Journal of Art & Design Education 35, 2 (2016), 183–195.Google ScholarCross Ref
- [56] . 2020. Art, social justice and critical pedagogy in educational research: The portrait of an artist as a young person. In Handbook of Qualitative Research in Education. Edward Elgar Publishing.Google Scholar
- [57] . 2018. From digital consumption to digital invention: Toward a new critical theory and practice of multiliteracies. Theory into Practice 57, 1 (2018), 12–19.Google ScholarCross Ref
- [58] . 2020. Imagining a more just world: Critical arts pedagogy and youth participatory action research. International Journal of Qualitative Studies in Education 33, 1 (2020), 32–49.Google ScholarCross Ref
- [59] . 2021. Countering dominant discourses about youth: Critical arts approaches to analyze ideological and institutional oppressions. In Engaging Youth in Critical Arts Pedagogies and Creative Research for Social Justice. NY: Routledge, 23–46.Google Scholar
- [60] . 2015. If art education then critical digital making: Computational thinking and creative code. Studies in Art Education 57, 1 (2015), 21–38.Google ScholarCross Ref
- [61] . 2014. “I want them to feel the fear…”: Critical computational literacy as the new multimodal composition. In Exploring Multimodal Composition and Digital Writing. IGI Global, 364–378.Google ScholarCross Ref
- [62] . 2020. Code for what? In Popular Culture and the Civic Imagination: A Ccasebook, (Eds.). New York University Press, New York, NY, 89--99. For more on the concept of Civic Imagination, please see https://www.civicimaginationproject.org/.Google ScholarCross Ref
- [63] . 2016. Social design experiments: Toward equity by design. Journal of the Learning Sciences 25, 4 (2016), 565–598.Google ScholarCross Ref
- [64] . 1994. Grounded theory methodology. Handbook of Qualitative Research 17, 1 (1994), 273–285.Google Scholar
- [65] . 2005. Youth radio and the pedagogy of collegiality. Harvard Educational Review 75, 4 (2005), 409–434.Google ScholarCross Ref
- [66] . 2017. Emergent Strategy: Shaping Change, Changing Worlds. AK Press, Chico, CA.Google Scholar
- [67] . 2013. Gradual Release of Responsibility Instructional. Framework. ASCD. Retrieved from https://pdo.ascd.org/lmscourses/pd13oc005/media/formativeassessmentandccswithelaliteracymod_3-reading3.pdf.Google Scholar
- [68] . 2019. How to Use “I Do – We Do – You Do” in Teaching. Grand Canyon University. Retrieved from https://www.gcu.edu/blog/engineering-technology/how-use-i-do-we-do-you-do-teaching.Google Scholar
- [69] . APA Dictionary of Psychology. 2020. Retrieved from the American Psychological Association's online dictionary.Google Scholar
Index Terms
- In the Black Mirror: Youth Investigations into Artificial Intelligence
Recommendations
Exploring Why Underrepresented Students Are Less Likely to Study Machine Learning and Artificial Intelligence
ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1There is little research on why underrepresented minorities are less likely to specifically study Machine Learning and Artificial Intelligence (ML/AI). We surveyed 159 undergraduate students about their interest in, exposure to, and personal views on ML/...
Introducing Artificial Intelligence Fundamentals with LearningML: Artificial Intelligence made easy
TEEM'20: Eighth International Conference on Technological Ecosystems for Enhancing MulticulturalityThis paper is a summary of the webinar hold on October 22nd at the “Computational thinking and robotics in education” track in which the LearningML project was presented. The LearningML project aims to bring the fundamentals of Artificial Intelligence (...
Attitudes and perspectives towards the preferences for artificial intelligence in psychotherapy
AbstractThe use of artificial intelligence (AI) in psychotherapy has been increased in recent years. While these technologies in psychotherapy are growing, the circumstances of accepting artificial tools during psychotherapy need to be ...
Highlights- We explored the factors of choosing AI-based psychotherapy.
- The less stigma and ...
Comments