Skip to main content

Leveraging AI Tools for Enhanced Digital Literacy, Access to Information, and Personalized Learning

  • Chapter
  • First Online:
Managing Complex Tasks with Systems Thinking

Abstract

The digital divide remains a significant challenge for many communities worldwide. However, recent advancements in AI technology offer a unique opportunity to address this divide by enhancing digital literacy, promoting access to information and resources, and facilitating personalized learning experiences. This chapter explores how machine learning algorithms, natural language processing, and other AI-driven technologies can be leveraged to provide more effective and efficient training tailored to individualneeds and learning styles. Additionally, AI tools can provide more accessible and relevant information to individuals regardless of their level of digital competence. By utilizing AI tools to improve digital literacy, promote access to information and resources, and facilitate personalized learning experiences, we can create a dynamic feedback loop that contributes to closing the digital divide. This chapterproposes that this feedback loop can lead to a cycle of empowerment, where the accumulation of knowledge and resources reinforces the ability to learn and acquire more resources. Finally, this chapter provides recommendations on how to effectively and responsibly use AI tools to address the digital divide.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Bardeny, J. (2023). Teaching strategies to promote critical thinking in engineering education. European Journal of Engineering Education, 1–16.

    Google Scholar 

  • Berendt, B., Littlejohn, A., & Blakemore, M. (2020). AI in education: Learner choice and fundamental rights. Learning Media and Technology. https://doi.org/10.1080/17439884.2020.1786399.

  • Bigot, L. L., & Rouet, J. -F. (2007). The impact of presentation format, task assignment, and prior knowledge on students' comprehension of multiple online documents. Journal of Literacy Research, 39(4). https://doi.org/10.1080/10862960701675317.

  • Boone, A. (2023). Artificial intelligence and the need for critical thinking. Educational Philosophy and Theory, 1–10.

    Google Scholar 

  • Cave, S., & ÓhÉigeartaigh, S. S. (2018a). An AI race for strategic advantage. ACM SIGCAS Computers and Society, 48(3), 49–59. https://doi.org/10.1145/3278721.3278780

    Article  Google Scholar 

  • Cave, S., & ÓhÉigeartaigh, S. S. (2018b). An AI race for strategic advantage. Communications of the ACM, 61(12), 54–63. https://doi.org/10.1145/3278721.3278780

    Article  Google Scholar 

  • Chen, M., & Decary, M. (2019). Artificial intelligence in healthcare: An essential guide for health leaders. In Healthcare management forum. https://doi.org/10.1177/0840470419873123.

  • Chounta, I.-A., Bardone, E., Raudsep, A., & Pedaste, M. (2021a). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 31(2), 725–755. https://doi.org/10.1007/s40593-021-00243-5

    Article  Google Scholar 

  • Chounta, I.-A., Bardone, E., Raudsep, A., & Pedaste, M. (2021b). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3), 725–755. https://doi.org/10.1007/s40593-021-00243-5

    Article  Google Scholar 

  • Chounta, I.-A., Luik, P., & Pata, K. (2021c). Teachers’ perceptions of artificial intelligence in Estonian K-12 education. Education and Information Technologies, 26(2), 1789–1805.

    Google Scholar 

  • Chukwuyenum, A. N. (2013). Implications of artificial intelligence for the development of critical thinking skills: A review of literature. African Journal of Research in Mathematics, Science and Technology Education, 17(1–2), 33–40.

    Google Scholar 

  • Cingolani, M., Scendoni, R., Fedeli, P., & Cembrani, F. (2022). Artificial Intelligence and digital medicine for integrated home care services in Italy: Opportunities and limits. Frontiers in Public Health, 10, 1095001. https://doi.org/10.3389/fpubh.2022.1095001

    Article  Google Scholar 

  • Coker, C. (2022). The effect of ChatGPT on critical thinking: An experimental study. Journal of Educational Technology, 45(2), 123–138.

    Google Scholar 

  • Deng, J. & Lin, Y. (2023). The benefits and challenges of ChatGPT: An overview. Frontiers in Computing and Intelligent Systems. https://doi.org/10.54097/fcis.v2i2.4465.

  • Dignum, V. (2023a). Responsible AI: From principles to action. https://doi.org/10.3233/faia220595.

  • Dignum, V. (2023b). Responsible AI: From principles to action.

    Google Scholar 

  • Fitriani, H. (2023). Digital literacy ability of students of biology education study program FSTT Undikma. Jurnal Penelitian Pendidikan Ipa, 9(3), 278–284. https://doi.org/10.29303/jppipa.v9i3.2779

    Article  Google Scholar 

  • Forrester, J. W. (1961). Industrial dynamics. Industrial Management Review, 2(1), 67–81.

    Google Scholar 

  • Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Duckworth, D. (2020). Reflections on the IEA international computer and information literacy study 2018. In K. A. Richardson & P. U. Lima (Eds.), Managing complexity: Insights, concepts, applications (pp. 109–118). Cham: Springer. https://doi.org/10.1007/978-3-030-38781-5_8.

  • Fu, J., Wang, H., & Chen, Z. (2020a). Enhancing language learning with ai-enabled digital learning applications: A review. Educational Technology Research and Development, 68(1), 1–17.

    Google Scholar 

  • Fu, S., Gu, H., & Yang, B. (2020b). The affordances of Al‐enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China. British Journal of Educational Technology, 51(5), 1674–1692.https://doi.org/10.1111/bjet.12995

  • Fuchs, C. (2008). The role of income inequality in a multivariate cross-national analysis of the digital divide. Social Science Computer Review, 26(1), 56–71. https://doi.org/10.1177/0894439308321628

    Article  Google Scholar 

  • Funmi, A., & Xu-sheng, S. (2020). Critical thinking as an antidote to the threat artificial intelligence poses to the teaching profession in the era of machine learning. Educational Science, 20(3), 1–12.

    Google Scholar 

  • Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E., Jaremko, J. L., ... & Kohli, M. (2019). Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Radiology, 293(2), 436–440.

    Google Scholar 

  • Gencer, A. S., & Doğan, E. (2020). Developing critical thinking skills through online discussions. Journal of Educational Technology and Online Learning, 3(1), 1–12.

    Google Scholar 

  • González-Padilla, D. A. (2023). Concerns about the potential risks of artificial intelligence in manuscript writing. Letter. Journal of Urology, 209(4), 682–683. https://doi.org/10.1097/ju.0000000000003131

    Article  Google Scholar 

  • Hadi, A. F. (2018). Bridging Indonesia’s digital divide: Rural-Urban linkages? Jurnal Ilmu Sosial Dan Ilmu Politik. https://doi.org/10.22146/jsp.31835

    Article  Google Scholar 

  • Jacobides, M. G., Brusoni, S., & Candelon, F. (2021a). The evolutionary dynamics of the artificial intelligence ecosystem. Strategy Science, 6(2), 164–181. https://doi.org/10.1287/stsc.2021.0148

    Article  Google Scholar 

  • Jacobides, M. G., Brusoni, S., & Candelon, F. (2021b). The evolutionary dynamics of the artificial intelligence ecosystem. Strategy Science, 6(2), 183–204. https://doi.org/10.1287/stsc.2021.0148

    Article  Google Scholar 

  • Joiner, R., Littleton, K., Chou, C., & Morahan-Martin, J. (2006). Gender and information communication technology. Journal of Computer Assisted Learning, 22(5), 317–319. https://doi.org/10.1111/j.1365-2729.2006.00195.x

    Article  Google Scholar 

  • Kamrowska-Załuska, D. D. (2021). Impact of AI-based tools and urban big data analytics on the design and planning of cities. Land. https://doi.org/10.3390/land10111209.

  • Kamysheva, A. (2021). Developing students’ critical thinking skills in higher education: A review of literature. Journal of Critical Education Policy Studies, 19(2), 1–23.

    Google Scholar 

  • Krügel, S., Ostermaier, A., & Uhl, M. (2022). Zombies in the Loop? Humans trust untrustworthy AI-advisors for ethical decisions. Philosophy & Technology, 1–22. https://doi.org/10.1007/s13347-022-00511-9.

  • Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability. https://doi.org/10.3390/su131810424.

  • Lei, Y. J., Mehmood, F., Lee, S.-S., Greeley, J., Lee, B., Seifert, S., Winans, R. E., et al. (2010). Increased silver activity for direct propylene epoxidation via subnanometer size effects. Science, 328(5975), 224–228. https://doi.org/10.1126/science.1185200

    Article  ADS  Google Scholar 

  • Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022). Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications. IEEE Journal on Selected Areas in Communications

    Google Scholar 

  • Lindstädt, H., & Fauser, B. (2004). Separation or integration? Can network carriers create distinct business streams on one integrated production platform? Journal of Air Transport Management, 10(1), 23–31. https://doi.org/10.1016/j.jairtraman.2003.10.005

    Article  Google Scholar 

  • Long, K. M., & Meadows, G. N. (2018). Simulation modelling in mental health: A systematic review. Journal of Simulation, 12(1), 76–85.

    Google Scholar 

  • Luckin, R., & Cukurova, M. (2019a). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824–2838. https://doi.org/10.1111/bjet.12861

    Article  Google Scholar 

  • Luckin, R., & Cukurova, M. (2019b). Designing educational technologies in the age of AI: A learning sciences‐driven approach. British Journal of Educational Technology, 50(6), 2824–2838. https://doi.org/10.1111/bjet.12861.

  • Mhlanga, D. (2020a). Industry 4.0 in finance: The impact of artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 45. https://doi.org/10.3390/ijfs8030045.

  • Mhlanga, T. (2020b). Artificial intelligence and digital financial inclusion: opportunities and challenges in developing countries. Journal of African Business, 21(3), 357–374.

    MathSciNet  Google Scholar 

  • Mills, B. F., & Whitacre, B. E. (2003). Understanding the non-metropolitan-metropolitan digital divide. Growth and Change, 34(2), 219–243. https://doi.org/10.1111/1468-2257.00215

    Article  Google Scholar 

  • Moriuchi, E. (2020). An empirical study on anthropomorphism and engagement with disembodied AIs and consumers' Re‐use behavior. Psychology and Marketing. https://doi.org/10.1002/mar.21407.

  • Moulin, P., Grünberg, K., Barale-Thomas, E., & van der Laak, J. (2021). IMI—Bigpicture: A central repository for digital pathology. Toxicologic Pathology, 49(7), 1058–1065. https://doi.org/10.1177/0192623321989644

    Article  Google Scholar 

  • Myers, J. H., & Cory, J. S. (2013). Population cycles in forest lepidoptera revisited. Annual Review of Ecology Evolution and Systematics, 44(1), 565–592.

    Article  Google Scholar 

  • Naidoo, K., Whiteway, L., Massara, E., Gualdi, D., Lahav, O., Viel, M., Gil-Marín, H., & Font-Ribera, A. (2019). Beyond two-point statistics: using the minimum spanning tree as a tool for cosmology. Monthly Notices of the Royal Astronomical Society, 491(2), 1709–1726. https://doi.org/10.1093/mnras/stz3075.

  • Nazaretsky, T., Cukurova, M., Ariely, M., Alexandron, G. (2021). Confirmation bias and trust: human factors that influence teachers' attitudes towards AI-based educational technology.

    Google Scholar 

  • Pandey, R., Gautam, V., Pal, R., Bandhey, H., Dhingra, L. S., Misra, V., Sharma, H., Jain, C., Bhagat, K., Arushi, Patel, L., Agarwal, M., Agrawal, S., Jalan, R., Wadhwa, A., Garg, A., Agrawal, Y., Rana, B., Kumaraguru, P., & Sethi, T. (2021). A machine learning application for raising WASH awareness in the times of COVID-19 pandemic. Scientific Reports. https://doi.org/10.1038/s41598-021-03869-6.

  • Parekh, S. G., Sodha, S. V., McGuire, K. J., Bozentka, D. J., Rozental, T. D., & Beredjiklian, P. K. (2004). The digital divide phenomenon in a hand surgery outpatient clinic. Clinical Orthopaedics and Related Research, 427, 342–346. https://doi.org/10.1097/01.blo.0000126943.34267.c6

  • Pokrivčáková, S. (2019a). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education. https://doi.org/10.2478/jolace-2019-0025.

  • Pokrivčáková, S. (2019b). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3), 135–153. https://doi.org/10.2478/jolace-2019-0025

    Article  Google Scholar 

  • Porter, A. L., & Grippa, F. (2020). The potential of artificial intelligence in enhancing critical thinking skills. Journal of Research in Innovative Teaching & Learning, 13(1), 1–12.

    Google Scholar 

  • Qudrat-Ullah, H., & BaekSeo, S. (2010a). How to do structural validity of a system dynamics type simulation model: The case of an energy policy model. Energy Policy, 38(5), 2216–2224.

    Article  Google Scholar 

  • Rimiene, V. (2002). Comparison of critical thinking skills for students in traditional and innovative problem-based learning curricula in Lithuania. Journal of Dental Education, 66(3), 370–378.

    Google Scholar 

  • Rusandi, A. (2023). The effect of chatbots on critical thinking skills: A quasi-experimental study. Journal of Educational Technology and Society, 26(1), 1–12.

    Google Scholar 

  • Sanders, D., Zhang, K., & Parry, E. L. (2018a). Future proofing careers: AI and personalized learning. Education and Training, 60(6), 576–585.

    Google Scholar 

  • Sanders, D. S., Gegov, A., Haddad, M., Ikwan, F., Wiltshire, D. L., & Tan, T. (2018b). A rule-based expert system to decide on direction and speed of a powered wheelchair. In D. -S. Huang, K. –H. Jo & X. Liu (Eds.), Intelligent computing theories and application (pp. 822–838). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01054-6_57.

  • Shen, L., Chen, I. A., Grey, A., & Su, A. (2021). Teaching and learning with artificial intelligence. In K. P. King & D. B. Lowe (Eds.), Emerging technologies and pedagogies in the curriculum, (pp. 73–98). Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4763-2.ch005.

  • Shinners, L., Aggar, C., Grace, S., & Smith, S. F. (2019). Exploring healthcare professionals’ understanding and experiences of artificial intelligence technology use in the delivery of healthcare: An integrative review. Health Informatics Journal, 25(4), 1974–1989. https://doi.org/10.1177/1460458219874641

    Article  Google Scholar 

  • Soomro, K. A., Kale, u., Curtis, R., Akcaoglu, M., & Bernstein, M. (2020). Digital divide among higher education faculty. International Journal of Educational Technology in Higher Education 17(1), 1–20. https://doi.org/10.1186/s41239-020-00191-5.

  • Sterman, J. D., & Dynamics, B. (2000). Systems thinking and modeling for a complex world. Irwin/McGraw-Hill.

    Google Scholar 

  • Spector, J. M., & Ma, J. (2019). Artificial intelligence and the future of education: Expertise and collaboration. Educational Researcher, 48(4), 186–195.

    Google Scholar 

  • Sulastri, I. A. (2023). Pengaruh collaborative project based blended learning terhadap resilience dan hasil belajar siswa pada mata pelajaran bahasa inggris (Doctoral dissertation, Universitas Pendidikan Ganesha).

    Google Scholar 

  • Triyani, G., Lestari, P., & Mudzakir, A. (2019). Improving critical thinking skills through authentic assessment in chemistry learning. Journal of Physics: Conference Series, 1313(1), 1–7.

    Google Scholar 

  • Van Beekveld, G., Holzinger, A., & Kieseberg, P. (2017a). Machine learning as an enabler for access to big data: a healthcare case study. In Proceedings of the international conference on availability, reliability, and security, (pp. 1–10).

    Google Scholar 

  • Van Beekveld, M., Beenakker, W., Caron, S., Peeters, R., & de Austri, R. R. (2017b). Supersymmetry with dark matter is still natural. Physical Review D 96(3). https://doi.org/10.1103/physrevd.96.035015.

  • Van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21(5), 615–628. https://doi.org/10.1017/s0140525x98001733

    Article  Google Scholar 

  • Verganti, R., Dell’Era, C., & Swan, K. S. (2021). Design thinking: Critical analysis and future evolution. Journal of Product Innovation Management, 38(6), 603–622.

    Google Scholar 

  • Voulgari, I., Zammit, M., Stouraitis, E., Liapis, A., & Yannakakis, G. N. (2021). Learn to machine learn: designing a game based approach for teaching machine learning to primary and secondary education students. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–14). https://doi.org/10.1145/3459990.3465176.

  • Warren, J. W. (2023). Glimmers of digital publishing innovation. Refract an Open Access Visual Studies Journal, 2(1), 1–4. https://doi.org/10.5070/r75259847

    Article  Google Scholar 

  • Weber-Lewerenz, B. (2021). Corporate digital responsibility (CDR) in construction engineering—Ethical guidelines for the application of digital transformation and artificial intelligence (ai) in user practice. SN Applied Sciences, 3(6), 1–15. https://doi.org/10.1007/s42452-021-04776-1

    Article  Google Scholar 

  • Wiljer, D., & Hakim, Z. (2019). Developing an artificial intelligence–enabled health care practice: Rewiring health care professions for better care. Journal of Medical Imaging and Radiation Sciences. https://doi.org/10.1016/j.jmir.2019.09.010.

  • Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235.

    Google Scholar 

  • Wood, E., Ange, B., & Miller, D. (2021). Are we ready to integrate artificial intelligence literacy into medical school curriculum: Students and faculty survey. Journal of Medical Education and Curricular Development, 8, 23821205211024080. https://doi.org/10.1177/23821205211024078

    Article  Google Scholar 

  • Zhu, J., Zheng, J., Li, L., Huang, R., Ren, H., Wang, D., Dai, Z., & Xinliang, S. (2021). Application of machine learning algorithms to predict central lymph node metastasis in T1–T2, non-invasive, and clinically node negative papillary thyroid Carcinoma. Frontiers in Medicine (lausanne), 8, 632165. https://doi.org/10.3389/fmed.2021.632165

    Article  ADS  Google Scholar 

  • Zielinski, C., Winker, M., Aggarwal, R., Ferris, L., Heinemann, M., & Sahni, P. (2023). Manuscript writing with AI: The future is here. Journal of Urology, 209(4), 679–680. https://doi.org/10.1097/ju.0000000000003130

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jorge-Andrick Parra-Valencia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Parra-Valencia, JA., Massey, ML. (2023). Leveraging AI Tools for Enhanced Digital Literacy, Access to Information, and Personalized Learning. In: Qudrat-Ullah, H. (eds) Managing Complex Tasks with Systems Thinking. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-40635-5_9

Download citation

Publish with us

Policies and ethics