nach oben

Education and Information Technologies

Erschienen in:

22.10.2022

Evaluating an artificial intelligence literacy programme for empowering and developing concepts, literacy and ethical awareness in senior secondary students

verfasst von: Siu-Cheung Kong, William Man-Yin Cheung, Olson Tsang

Erschienen in: Education and Information Technologies | Ausgabe 4/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Artificial intelligence (AI) literacy education for senior secondary students can prepare them for an AI-pervasive future. Although senior secondary students have been targeted, whether they can learn abstract AI concepts, feel empowered to harness AI and understand AI ethical issues is under-researched. We report a 34-h AI literacy programme with three courses for senior secondary students, examining whether they can acquire machine learning, deep learning concepts and discuss related ethical issues in project-based learning. Fifty-nine, fifty-one and thirty-one students completed these three courses, respectively, with pre- and post-course tests, surveys and self-reflective writing measuring their performance. Concepts tests and self-reflective writing showed that the programme improved the senior secondary students’ AI concepts and ethical awareness. Programming knowledge was not a prerequisite for conceptual understanding and empowerment. The programme did not alter the students’ self-perceived comprehension of ethical principles in any year of study, suggesting that it is a challenge for senior secondary students to fully understand these abstract, higher-level concepts. The curriculum design and content coverage of the programme will be further refined.

Vorheriger Artikel Combining latent profile analysis and programming traces to understand novices’ differences in debugging

Nächster Artikel Contemporary american literature in Online Learning: fostering Reading Motivation and Student Engagement

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Aharony, N., & Gazit, T. (2018). Students’ information literacy self-efficacy: An exploratory study. Journal of Librarianship and Information Science, 52(1), 224–236. https://doi.org/10.1177/0961000618790312CrossRef

Ali, S., DiPaola, D., Lee, I., Sindato, V., Kim, G., Blumofe, R., & Breazeal, C. (2021). Children as creators, thinkers and citizens in an AI-driven future. Computers and Education: Artificial Intelligence, 2, 100040. https://doi.org/10.1016/j.caeai.2021.100040CrossRef

Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives (Complete). Longman.

Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022). Ethical framework for artificial intelligence and digital technologies. International Journal of Information Management, 62, 102433. https://doi.org/10.1016/j.ijinfomgt.2021.102433CrossRef

Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. https://doi.org/10.1037/0003-066X.37.2.122CrossRef

Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080CrossRef

Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2022). Explicating AI literacy of employees at digital workplaces. IEEE Transactions on Engineering Management. Advance online publication. https://doi.org/10.1109/TEM.2021.3138503

Chiu, T. K. F. (2021). A holistic approach to the design of artificial intelligence (AI) education for K-12 schools. TechTrends, 65(5), 796–807. https://doi.org/10.1007/s11528-021-00637-1CrossRef

Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., & Yam, Y. (2022). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30–39. https://doi.org/10.1109/TE.2021.3085878CrossRef

de Freitas, A. A., & Weingart, T. B. (2021). I'm going to learn what?!?: Teaching artificial intelligence to freshmen in an introductory computer science course. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 198–204). ACM. https://doi.org/10.1145/3408877.3432530

Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. In Proceedings of FabLearn 2019 (pp. 104–111). ACM. https://doi.org/10.1145/3311890.3311904

Education Bureau. (2017). Mathematics Education: Key Learning Area Curriculum Guide (Primary 1– Secondary 6). Education Bureau. https://www.edb.gov.hk/attachment/en/curriculum-development/kla/ma/curr/ME_KLACG_eng_2017_12_08.pdf

Education Bureau. (2021). Student Enrolment Statistics, 2020/21 (Kindergartens, Primary and Secondary Schools). Education Bureau. https://www.edb.gov.hk/attachment/en/about-edb/publications-stat/figures/Enrol_2020.pdf

European Commission’s Joint Research Centre (JRC), & Organisation for Economic Co-operation and Development (OECD). (2021). National Strategies on Artificial Intelligence: A European Perspective. Publications Office of the European Union. https://doi.org/10.2760/069178

European Group on Ethics (EGE) in Science and New Technologies. (2018). Statement on Artificial Intelligence, Robotics and 'Autonomous' Systems. Publications Office of the European Union. https://doi.org/10.2777/786515

Estevez, J., Garate, G., & Grana, M. (2019). Gentle introduction to artificial intelligence for high-school students using Scratch. IEEE Access, 7, 179027–179036. https://doi.org/10.1109/ACCESS.2019.2956136CrossRef

Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1

Fosso Wamba, S., Bawack, R. E., Guthrie, C., Queiroz, M. M., & Carillo, K. D. A. (2021). Are we preparing for a good AI society? A bibliometric review and research agenda. Technological Forecasting and Social Change, 164, 120482. https://doi.org/10.1016/j.techfore.2020.120482CrossRef

Frymier, A. B., Shulman, G. M., & Houser, M. (1996). The development of a learner empowerment measure. Communication Education, 45(3), 181–199. https://doi.org/10.1080/03634529609379048CrossRef

Greenwald, E., Leitner, M., & Wang, N. (2021). Learning artificial intelligence: Insights into how youth encounter and build understanding of AI concepts. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 15526–15533). AAAI. https://ojs.aaai.org/index.php/AAAI/article/view/17828

Gresse von Wangenheim, C., Hauck, J. C. R., Pacheco, F. S., & Bertonceli Bueno, M. F. (2021). Visual tools for teaching machine learning in K-12: A ten-year systematic mapping. Education and Information Technologies, 26(5), 5733–5778. https://doi.org/10.1007/s10639-021-10570-8CrossRef

Han, J., Usher, E. L., & Brown, C. S. (2021). Trajectories in quantitative and humanities self-efficacy during the first year of college. Learning and Individual Differences, 91, 102054. https://doi.org/10.1016/j.lindif.2021.102054CrossRef

Henry, J., Hernalesteen, A., & Collard, A.-S. (2021). Teaching artificial intelligence to K-12 through a role-playing game questioning the intelligence concept. KI - Künstliche Intelligenz, 35, 171–179. https://doi.org/10.1007/s13218-021-00733-7CrossRef

Herrington, J., & Oliver, R. (2000). An instructional design framework for authentic learning environments. Educational Technology Research and Development, 48(3), 23–48. https://doi.org/10.1007/BF02319856CrossRef

Hess, R. D., Torney, J. V., & Valsiner, J. (2017). The development of political attitudes in children (1st ed.). Routledge. https://doi.org/10.4324/9781315131726CrossRef

Hitron, T., Orlev, Y., Wald, I., Shamir, A., Erel, H., & Zuckerman, O. (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 (pp. 1–11). ACM. https://doi.org/10.1145/3290605.3300645

High Level Expert Group (HLEG) on Artificial Intelligence. (2019). Ethics guidelines for trustworthy AI. Publications Office of the European Union. https://doi.org/10.2759/177365

Hong Kong Education City. (2021). Go AI scheme. https://www.edcity.hk/ai/en

Jang, Y., Choi, S., & Kim, H. (2022). Development and validation of an instrument to measure undergraduate students’ attitudes toward the ethics of artificial intelligence (AT-EAI) and analysis of its difference by gender and experience of AI education. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-022-11086-5

Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493CrossRef

Jiang, H., Turnbull, D., Wang, X., Chugh, R., Dou, Y., & Chen, S. (2022b). How do mathematics interest and self-efficacy influence coding interest and self-efficacy? A structural equation modeling analysis. International Journal of Educational Research, 115, 102058. https://doi.org/10.1016/j.ijer.2022.102058CrossRef

Jiang, H., Chugh, R., Turnbull, D., Wang, X., & Chen, S. (2022a). Modeling the impact of intrinsic coding interest on STEM career interest: Evidence from senior high school students in two large Chinese cities. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-022-11277-0

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399. https://doi.org/10.1038/s42256-019-0088-2CrossRef

Kim, S., Jang, Y., Kim, W., Choi, S., Jung, H., Kim, S., & Kim, H. (2021). Why and what to teach: AI curriculum for elementary school. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 15569–15576). AAAI. https://ojs.aaai.org/index.php/AAAI/article/view/17833

Kong, S.-C. (2021). Delivery and evaluation of an e-Learning framework through computer-aided analysis of learners’ reflection text in a teacher development course. Research and Practice in Technology Enhanced Learning, 16, 28. https://doi.org/10.1186/s41039-021-00172-wCrossRef

Kong, S.-C., Li, P., & Song, Y. (2018). Evaluating a bilingual text-mining system with a taxonomy of key words and hierarchical visualization for understanding learner-generated text. Journal of Educational Computing Research, 56(3), 369–395. https://doi.org/10.1177/0735633117707991CrossRef

Kong, S.-C., Cheung, W.M.-Y., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026CrossRef

Kong, S.-C., Cheung, W.M.-Y., & Zhang, G. (2022). Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building. Computers in Human Behavior Reports, 7, 100223. https://doi.org/10.1016/j.chbr.2022.100223CrossRef

Kong, S.-C., Cheung, W.M.-Y., & Zhang, G. (2023). Evaluating an artificial intelligence literacy programme for developing university students’ conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology and Society, 26(1), 16–30.

Kong, S.-C., & Zhang, G. (2021). A conceptual framework for designing artificial intelligence literacy programmes for educated citizens. In S.-C. Kong, Q. Wang, R. Huang, Y. Li, & T.-C. Hsu (Eds.), Conference proceedings (English paper) of the 25th Global Chinese Conference on Computers in Education (GCCCE 2021) (pp. 11–15). The Education University of Hong Kong.

Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021). Developing middle school students' AI literacy. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 191–197). ACM. https://doi.org/10.1145/3408877.3432513

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

Long, D., Blunt, T., & Magerko, B. (2021). Co-Designing AI literacy exhibits for informal learning spaces. In Proceedings of the ACM on Human-Computer Interaction (Vol. 5, pp.1–35). ACM. https://doi.org/10.1145/3476034

National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (NCPHS). (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. NCPHS. https://www.hhs.gov/ohrp/sites/default/files/the-belmont-report-508c_FINAL.pdf

Ng, D. T. K., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022). Using digital story writing as a pedagogy to develop AI literacy among primary students. Computers and Education: Artificial Intelligence, 3, 100054. https://doi.org/10.1016/j.caeai.2022.100054CrossRef

Organisation for Economic Co-operation and Development (OECD). (2019a). Recommendation of the Council on Artificial Intelligence. OECD Publishing. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449

Organisation for Economic Co-operation and Development (OECD). (2019b). Skills matter: Additional results from the survey of adult skills. OECD Publishing. https://doi.org/10.1787/1f029d8f-en

Organisation for Economic Co-operation and Development (OECD). (2021). Education and AI: Preparing for the Future & AI, Attitudes and Values. OECD Publishing. https://www.oecd.org/education/2030-project/about/documents/Education-and-AI-preparing-for-the-future-AI-Attitudes-and-Values.pdf

Park, Y., & Shin, Y. (2021). Tooee: A novel Scratch extension for K-12 big data and artificial intelligence education using text-based visual blocks. IEEE Access, 9, 149630–149646. https://doi.org/10.1109/ACCESS.2021.3125060CrossRef

Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000366994

Poursharif, G., Doss, T. P., Broadbent, R., & Knight, G. (2021). Developing global engineers through interdisciplinary PBL and design thinking. In T. Klinger, C. Kollmitzer, & A. Peste (Eds.), Proceedings of the 2021 IEEE Global Engineering Education Conference (EDUCON) (pp. 194–198). IEEE. https://doi.org/10.1109/EDUCON46332.2021.9453869

Prince, M., & Felder, R. (2007). The many faces of inductive teaching and learning. Journal of College Science Teaching, 36(5), 14–20. https://my.nsta.org/click?file=jcst0703_14.pdf

Prunkl, C. (2022). Human autonomy in the age of artificial intelligence. Nature Machine Intelligence, 4(2), 99–101. https://doi.org/10.1038/s42256-022-00449-9CrossRef

Sabouret, N., & De Assis, L. (2020). Understanding artificial intelligence (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781003080626CrossRef

Stadelmann, T., Keuzenkamp, J., Grabner, H., & Würsch, C. (2021). The AI-Atlas: Didactics for teaching AI and machine learning on-site, online, and hybrid. Education Sciences, 11(7), 318. https://doi.org/10.3390/educsci11070318CrossRef

Sullivan, B. M. (1988). A legacy for learners: The report of the royal commission on education. Royal Commission on Education. http://eduq.info/xmlui/handle/11515/18782

Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., & Valtonen, T. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE Access, 9, 110558–110572. https://doi.org/10.1109/ACCESS.2021.3097962CrossRef

Tierney, P., & Farmer, S. M. (2011). Creative self-efficacy development and creative performance over time. Journal of Applied Psychology, 96(2), 277–293. https://doi.org/10.1037/a0020952CrossRef

Tomlin, K. A., Metzger, M. L., & Bradley-Geist, J. (2019). Removing the blinders: Increasing students’ awareness of self-perception biases and real-world ethical challenges through an educational intervention. Journal of Business Ethics, 169, 731–746. https://doi.org/10.1007/s10551-019-04294-6CrossRef

Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 9795–9799). AAAI. https://doi.org/10.1609/aaai.v33i01.33019795

Tsekleves, E., Cosmas, J., & Aggoun, A. (2014). Benefits, barriers and guideline recommendations for the implementation of serious games in education for stakeholders and policymakers. British Journal of Educational Technology, 47(1), 164–183. https://doi.org/10.1111/bjet.12223CrossRef

United Nations Educational Scientific and Cultural Organization (UNESCO). (2022). K-12 AI curricula: A mapping of government-endorsed AI curricula. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000380602

VanderKaay, S., Letts, L., Jung, B., & Moll, S. E. (2019). On-line ethics education for occupational therapy clinician-educators: A single-group pre-/post-test study. Disability and Rehabilitation, 41(23), 2841–2853. https://doi.org/10.1080/09638288.2018.1473510CrossRef

Vartiainen, H., Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., & Valtonen, T. (2021). Machine learning for middle schoolers: Learning through data-driven design. International Journal of Child-Computer Interaction, 29, 100281. https://doi.org/10.1016/j.ijcci.2021.100281CrossRef

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the sustainable development goals. Nature Communications, 11(1), 233. https://doi.org/10.1038/s41467-019-14108-yCrossRef

Williams, R., Ali, S., Devasia, N., DiPaola, D., Hong, J., Kaputsos, S. P., Jordan, B., & Breazeal, C. (2022). AI + Ethics curricula for middle school youth: Lessons learned from three project-based curricula. International Journal of Artificial Intelligence in Education. Advance online publication. https://doi.org/10.1007/s40593-022-00298-y

World Intellectual Property Organization (WIPO). (2019). WIPO technology trends 2019 – Artificial Intelligence. WIPO. https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf

Wong, G., Ma, X., Dillenbourg, P., & Huan, J. (2020). Broadening artificial intelligence education in K-12: Where to start? ACM Inroads, 11(1), 20–29. https://doi.org/10.1145/3381884CrossRef

Yang, W. (2022). Artificial intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers and Education: Artificial Intelligence, 3, 100061. https://doi.org/10.1016/j.caeai.2022.100061MathSciNetCrossRef

Yi, Y. (2021). Establishing the concept of AI literacy: Focusing on competence and purpose. Jahr - European Journal of Bioethics, 12(2), 353–368. https://doi.org/10.21860/j.12.2.8CrossRef

Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2022). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education. Advance online publication. https://doi.org/10.1007/s40593-022-00293-3

Titel: Evaluating an artificial intelligence literacy programme for empowering and developing concepts, literacy and ethical awareness in senior secondary students
verfasst von: Siu-Cheung Kong
William Man-Yin Cheung
Olson Tsang
Publikationsdatum: 22.10.2022
Verlag: Springer US
Erschienen in: Education and Information Technologies / Ausgabe 4/2023
Print ISSN: 1360-2357
Elektronische ISSN: 1573-7608
DOI: https://doi.org/10.1007/s10639-022-11408-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2023

Comparing approaches for selection, development, and deployment of extended reality (XR) teaching applications: A case study at The University of Newcastle Australia

Blended classroom teaching equalizes educational inequality

Impact of instructors’ online teaching readiness on satisfaction in the emergency online teaching context

Computer science and non-computer science faculty members’ perception on teaching data science via an experiential learning platform

A systematic review of digital citizenship empirical studies for practitioners

Teaching bioengineering using a blended online teaching and learning strategy: a new pedagogy for adapting classrooms in developing countries

Premium Partner