AI Literacy for Primary and Middle School Teachers: History
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Contributor:
Leilei Zhao
,
Xiaofan Wu
,
Heng Luo

As smart technology promotes the development of various industries, artificial intelligence (AI) has also become an important driving force for innovation and transformation in education. For teachers, how to skillfully apply AI in teaching and improve their AI literacy has become a necessary goal for their sustainable professional development. 

  • artificial intelligence
  • literacy
  • teacher
  • structural equation modeling
  • sustainable development

1. Introduction

As artificial intelligence (AI) has begun to integrate into all aspects of daily life, the challenge of better preparing teachers’ education to employ intelligent technologies effectively and efficiently in schools has become a persistent issue [1][2]. Artificial intelligence has become the core technology in education, but it has no straightforward narrative from the 1950s until now. Progressive advancements in AI have generated intelligent machines and algorithms that can know and adapt in response to the environment and sets of rules which simulate human intelligence. Herein, AI refers to the science and technology of research and development of theories, methods, techniques, and application systems for simulating and extending human intelligence, which is connected to the challenge of utilizing computers to comprehend or imitate the intelligence of humans without any logical order or algorithm [3][4].
Traditionally, isolated ICT courses or units have been provided to promote teacher education, often supplied in early teachers’ improvement programs. These are conducted with the assumption that the strategy of “front loading” teachers with what is considered the necessary knowledge and skills to transform the information [5] will support them in meeting course assessment requirements, such as the growth of “technology-integrated” learning curricula for practical work in schools, and help them use AI technology productively in their future teaching careers [6][7]. Artificial intelligence technology has become an important driver for educational teaching reform and it affects teachers’ sustainable professional development. However, AI education courses and training opportunities are more available to students than teachers. Thus, to extend our understanding of AI literacy for developing the capabilities of teachers, the construction of AI literacy merits the special attention.
Similar to information literacy and digital literacy, AI literacy is a combination of “technology” and “literacy”, i.e., “artificial intelligence” and “literacy”, and is used to define skill sets across disciplines [8]. According to the study of Ng et al., AI literacy can be divided into four common dimensions to build structure: (1) the understanding level of fundamental AI concepts to support basic AI training; (2) the application of AI concepts in practical backgrounds to facilitate widespread AI education; (3) the evaluation and engagement with AI technologies within those contexts should be critical and reasonable; and (4) the ability to understand the endless ethical implications resulting from AI applications [9]. Based on the dimensions that already have been defined, here was analyzed the situation of teachers’ AI literacy. The AI literacy of teachers should be distinct from practical contexts with stricter requirements since these dimensions have not yet been targeted and tested in the course of teacher training [10].

2. Definition of Key Concepts

2.1. AI Literacy

Literacy was universally perceived as an ability that arises from learning skills including reading, speaking, and writing [11]. The appearance of knowledge based on AI technology education means that every learner should have the basic competencies of “intelligent literacy” to build a better foundation for integration into the digital society, so as to gain equity and respect in working life. This term has been extended to new areas of literacy, such as computer, information, digital, and AI literacy [12]. Information literacy can be taken as an example which empowers people in all walks of life to seek, evaluate, use, and create information effectively in order to achieve their personal goals, and mainly includes the skills of information management and the ability to apply information in an appropriate way [13][14]. In the twenty-first century, teachers with these technical skills can use advanced intelligent technology through computers to impart new knowledge and skills to students or colleagues in certain avant-garde ways [15][16][17].
Currently, with the advent of AI technologies, the application of AI has become critical and plays key roles across disciplines and industries [9][18]. Teachers need to know how to use AI technologies explicitly in their teaching, as well as to take ethical issues into consideration [19][20]. Thus, combining AI and literacy means that AI literacy is about the critical skills that individuals need to learn and live in the digital world with the help of AI-based technologies. According to Bloom, the educational components include the cognitive, psychomotor, and emotional domains, as well as the division of education in terms of knowledge, skills, and attitudes [21][22]. While AI literacy is not equal to the applications of AI, following the conclusion of Bloom, it includes four dimensions: Knowing and Understanding AI, Applying AI, Evaluating AI Application and AI Ethics, as identified from previous studies [23][24][25].

2.2. Knowing and Understanding AI

As teachers, the most significant AI literacy in education is about fundamental concepts, knowledge, information, and attitudes that require knowledge about AI. As the users of AI applications, teachers should prioritize learning the theoretical knowledge of AI instead of applying AI approaches at random and without prior intention. According to previous studies, AI literacy was initially defined as the ability to know the basic techniques and concepts of artificial intelligence embedded in different products and services [26][27]. A large number of teachers have been exposed to the technological environment with AI-enabled appliances, but in reality, they do not understand the fundamental concepts, such as big data structures, computational thinking concepts, or ubiquitous computing approaches. Sound knowledge about AI and principles of machine learning is so essential for their later careers that teachers should apply AI every classroom. Moreover, some researchers link AI literacy to learners’ self-perception, confidence, and readiness to learn AI. Meanwhile, a poor belief state in the potential of AI among teachers has also been proven [28][29]. To foster the AI literacy of learners, several scholars designed learning curricula and activities that promote AI literacy, paying attention to the way that teachers learned AI concepts [23][30], as well as understand AI. Thus, Knowing and Understanding AI (KUAI) is fundamental AI literacy that teachers should understand the basic concepts, knowledge, and instructions about using artificial intelligence in teaching.

2.3. Applying AI

It is obvious that educating teachers to know how to apply AI concepts and applications in different contexts is of great importance [23][30]. Machine learning applications have been evaluated after learning related curricula, which aims to educate citizens to understand AI applications and learn to adapt in their later careers, as well as understanding the ethical issues related to AI technologies [31][32]. Several studies have discussed the human-centered considerations and concentrated on using AI concepts and applications both regularly and ethically [33][34], which will be further discussed in H1. It is a struggle for teachers to guide students with the help of AI applications, since they still lack the ability to efficiently analyze data or integrate AI into instructional design [35][36]. AI thinking refers to building logic and algorithms to support teachers in understanding how to employ their knowledge in order to solve problems, work with unstructured data, and handle semantics issues in a scheduled way [37]. Taking How and Hung’s research into account, data analysis through computing allows teachers to make new discoveries from hidden patterns in the data through machine learning, which is a practical application of AI-based thinking [38]. For a teacher, applying AI is a literacy that educators utilize in terms of when and how to use AI in class exactly and correctly.

2.4. Evaluating AI Application

Compared with applying AI, the ability to evaluate AI application appropriately is more challenging to a teacher. In addition to understanding and using AI concepts in practice, AI literacy has been extended to two other competencies that enable teachers to evaluate AI technologies correctly and critically and to communicate, as well as collaborate effectively, with AI [8]. For example, enhancing teachers’ scientific and technological knowledge through evidence-based learning and continuous curriculum is crucial, since the aim of learning basic knowledge is to implement it in practice [37]. Individuals in the co-creation of AI facilities in public spaces could expand their literacy and experiences of public AI [39]. Teachers who are able to evaluate AI applications appropriately could inform, support, manipulate, and categorize AI concepts together in novel ways. After all, previous studies suggested slight variations on the definition of AI literacy, which support the idea that everyone should acquire basic AI knowledge and competencies, especially school teachers. Evaluating AI applications could be beneficial for enhancing their motivation and interest in teaching with AI [40]. Apart from the ethics of understanding and using AI, evaluating artificial intelligence applications is an important competency that enables teachers to judiciously evaluate AI technologies, commenting on the application of AI in teaching [41].

3. Relationships among the Key Concepts

3.1. Relationship between the Competencies of Knowing and Understanding AI and Evaluating AI Application

As to developing AI literacy, understanding AI concepts plays an essential role for learners to evaluate AI applications, since they had to invoke relevant concepts when interacting with AI services to ensure that the AI evaluation was in line with the educational technology. The content of basic AI concepts is uncertain while it has been discussed in several studies [12][38][42]. Deep learning, block chains, and machine learning are common AI concepts explored in the literature due to their broad applicability and huge influence. Technologies, such as machine learning, deep learning, and neural networks are listed as AI concepts required for AI literacy, and in addition to these, technologies such as blockchain and cloud computing are also important extensions of AI concepts [43][44]. Based on learners’ acceptance level, a few studies also conducted AI literacy courses for teachers, with topics such as machine learning and neural networks providing more advanced topics for later sections, stepping up the difficulty of the course to suit the learner’s knowledge development pattern [28][45].
In view of the complexity of AI concepts, support for learning artifacts is important to facilitate the understanding of AI evaluation and increase motivation and interest in learning AI knowledge. Recently, an improvement has emerged in hardware and software of computers and robots that enhance AI concepts available to educators [46]. Considering that Knowing and Understanding AI makes teachers learn more about how to apply AI properly, Evaluating AI Application is also significant based on teachers’ cognition of AI.

3.2. Relationships among Applying AI with Other Aspects

Besides emphasizing conceptual and literacy development for teachers, the AI literacy course has an important purpose: to equip participants with the basic skills to apply AI flexibly so that they can confidently engage in the digital world and integrate AI into their daily teaching. Through an understanding of how AI works, teachers will be able to think about how the use of AI can be integrated into education for the development of students and share these ideas with other peers. With initial mastery of AI, teachers will grow in confidence regarding AI use, naturally engage in critical discussions related to AI, and use AI to drive instructional change, thereby experiencing a degree of personal empowerment in the form of increased control over their own lives and coping skills [47]. In AI literacy courses, teachers often work to increase their confidence through exposure to AI, using AI concepts as a foundation and AI assessment to guide use, thus leading to a deeper and more thorough understanding of AI and clarifying the ethical aspects of AI educational applications [48]. Therefore, Applying AI (AAI) is essential to other aspects of AI literacy, directly influencing Knowing and Understanding AI (KUAI), Evaluating AI Application (EAIA) and AI Ethics (AIE).

3.3. Correlations among AI Ethics, Knowing and Understanding AI and Evaluating and Creating AI

Given that AI plays a significant role in everyday decision making, it may cause irreparable damage to society if it is not used properly [49]. Scientists and engineers, such as Elon Musk, have a more negative attitude toward AI, with their articulation of the horrors that could be caused by future AI technologies that would pose a serious threat to human existence within decades [50]. However, only a few studies mention the need to consider the concept of human-centered development and drew attention to educating citizens to become socially responsible [51]. Few studies focus on the need for the ethical AI paradox, which found that teachers paid no attention to ethical concerns, such as biased decisions, lack of transparency, privacy issues, and the risk of invasion of privacy [52][53]. Therefore, educators need to focus not only on improving their own AI application skills and interests but also on the potential social implications and ethical issues of AI. To prepare the next generation of responsible citizens for the future, teachers should compete for the use of AI in a reliable, credible, and equitable manner; prioritizing issues of equity, accountability, transparency, and ethics; and promoting the integration of computational technologies into teaching and learning in innovative and responsible ways, while drawing on areas with a sociotechnical orientation to reinvent the shape of education [54][55].
Artificial Intelligence literacy in teacher education is in its infancy. It emphasizes and regulates how teachers should understand AI concepts and apply AI. To expand the use of AI scenarios, there is a need to ensure that AI technologies are designed and utilized in a way that is both inclusive and equitable to addressing the underrepresentation of people of color and women in AI-using populations. Thus, the relationship among AI Ethics, Knowing and Understanding AI, and Evaluating AI Application should be found to affect teachers’ AI literacy.

This entry is adapted from the peer-reviewed paper 10.3390/su142114549

References

  1. Gallardo-Montes, C.P.; Rodríguez Fuentes, A.; Caurcel Cara, M.J.; Capperucci, D. Functionality of Apps for People with Autism: Comparison between Educators from Florence and Granada. Int. J. Environ. Res. Public Health 2022, 19, 7019.
  2. Chassignol, M.; Khoroshavin, A.; Klimova, A.; Bilyatdinova, A. Artificial Intelligence trends in education: A narrative overview. Procedia Comput. Sci. 2018, 136, 16–24.
  3. Bjola, C. AI for development: Implications for theory and practice. Oxf. Dev. Stud. 2022, 50, 78–90.
  4. Raisch, S.; Krakowski, S. Artificial Intelligence and management: The automation–augmentation paradox. Acad. Manag. Rev. 2021, 46, 192–210.
  5. Burns, M.K.; Egan, A.M.; Kunkel, A.K.; McComas, J.; Peterson, M.M.; Rahn, N.L.; Wilson, J. Training for generalization and maintenance in rti implementation: Front-loading for sustainability. Learn. Disabil. Res. Pract. 2013, 28, 81–88.
  6. Pacheco, N.C.; Aran, X.F.; Such, J. Attesting Digital Discrimination Using Norms. Int. J. Interact. Multimed. Artif. Intell. 2021, 6, 16–23.
  7. Appova, A.; Lee, H.J.; Bucci, T. Technology in the classroom: Banking education or opportunities to learn? Theory Into Pract. 2022, 61, 254–264.
  8. Long, D.; Magerko, B. What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 25–30 April 2020; pp. 1–16.
  9. Ng, D.T.K.; Leung, J.K.L.; Chu, K.W.S.; Qiao, M.S. AI literacy: Definition, teaching, evaluation and ethical issues. Proc. Assoc. Inf. Sci. Technol. 2021, 58, 504–509.
  10. Chen, M.; Zhou, C.; Wang, Y.; Li, Y. The role of school ICT construction and teacher information literacy in reducing teacher burnout: Based on SEM and fsQCA. Educ. Inf. Technol. 2022, 27, 8751–8770.
  11. McBride, C. Children’s Literacy Development: A Cross-Cultural Perspective on Learning to Read and Write; Routledge: London, UK, 2015.
  12. Zhang, S.; Wang, X.; Qi, Y. Artificial Intelligence Empowers Education Evaluation: The New Concept and Core Elements of “Integration of Academic Evaluation”. China Distance Educ. 2021, 2, 1–8; + 16 + 76.
  13. Hepworth, M.; Smith, M. Workplace information literacy for administrative staff in higher education. Aust. Libr. J. 2008, 57, 212–236.
  14. Fraillon, J.; Schulz, W.; Ainley, J. International Computer and Information Literacy Study: Assessment Framework; International Association for the Evaluation of Educational Achievement: Amsterdam, The Netherlands, 2013; pp. 15–19.
  15. Barak, M. Science teacher education in the twenty-first century: A pedagogical framework for technology-integrated social constructivism. Res. Sci. Educ. 2017, 47, 283–303.
  16. Cabero Almenara, J.; Fernández Batanero, J.M.; Barroso Osuna, J. Students of Teaching Degrees: ICTs and disabilities. Rev. Electrónica Investig. Educ. 2016, 18, 106–120.
  17. Hu, X.Y.; Xu, H.Y. Construction of Intelligent Education Literacy Framework for K-12 Teachers. Open Educ. Res. 2021, 4, 59–70.
  18. Sharifi, A.; Ahmadi, M.; Ala, A. The impact of Artificial Intelligence and digital style on industry and energy post-COVID-19 pandemic. Environ. Sci. Pollut. Res. 2021, 28, 46964–46984.
  19. Coghlan, S.; Miller, T.; Paterson, J. Good proctor or “big brother”? Ethics of online exam supervision technologies. Philos. Technol. 2021, 34, 1581–1606.
  20. Ng, D.T.K.; Leung, J.K.L.; Chu, S.K.W.; Qiao, M.S. Conceptualizing AI literacy: An exploratory review. Comput. Educ. Artif. Intell. 2021, 2, 100041.
  21. Tarman, B.; Kuran, B. Examination of the cognitive level of questions in social studies textbooks and the views of teachers based on bloom taxonomy. Educ. Sci.: Theory Pract. 2015, 15, 213–222.
  22. Agha, K.; Zhu, X.; Chikwa, G. Towards Academic Integrity: Using Bloom’s Taxonomy and Technology to Deter Cheating in Online Courses. In Technologies, Artificial Intelligence and the Future of Learning Post-COVID-19; Springer: Cham, Switzerland, 2022; pp. 447–466.
  23. Cope, B.; Kalantzis, M.; Searsmith, D. Artificial Intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educ. Philos. Theory 2021, 53, 1229–1245.
  24. Acemoglu, D.; Restrepo, P. Artificial Intelligence, automation, and work. In The Economics of Artificial Intelligence: An Agenda; University of Chicago Press: Chicago, IL, USA, 2018; pp. 197–236.
  25. Kong, S.C.; Cheung, W.M.Y.; Zhang, G. Evaluation of an Artificial Intelligence literacy course for university students with diverse study backgrounds. Comput. Educ. Artif. Intell. 2021, 2, 100026.
  26. Estevez, J.; Garate, G.; Graña, M. Gentle introduction to Artificial Intelligence for high-school students using scratch. IEEE Access 2019, 7, 179027–179036.
  27. Williams, R.; Ali, S.; Devasia, N.; DiPaola, D.; Hong, J.; Kaputsos, S.P.; Jordan, B.; Breazeal, C. AI+ ethics curricula for middle school youth: Lessons learned from three project-based curricula. Int. J. Artif. Intell. Educ. 2022, 1–59.
  28. Vazhayil, A.; Shetty, R.; Bhavani, R.R.; Akshay, N. Focusing on teacher education to introduce AI in schools: Perspectives and illustrative findings. In Proceedings of the 2019 IEEE Tenth International Conference on Technology for Education (T4E), Goa, India, 9–11 September 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 71–77.
  29. Edwards, C.; Edwards, A.; Spence, P.R.; Lin, X. I, teacher: Using Artificial Intelligence (AI) and social robots in communication and instruction. Commun. Educ. 2018, 67, 473–480.
  30. Shanthamallu, U.S.; Spanias, A. Machine and Deep Learning Algorithms and Applications. Synth. Lect. Signal Process. 2021, 12, 1–123.
  31. De Cremer, D.; Kasparov, G. The ethical AI—Paradox: Why better technology needs more and not less human responsibility. AI Ethics 2022, 2, 1–4.
  32. Baduge, S.K.; Thilakarathna, S.; Perera, J.S.; Arashpour, M.; Sharafi, P.; Teodosio, B.; Shringi, A.; Mendis, P. Artificial Intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Autom. Constr. 2022, 141, 104440.
  33. Ahmad, K.; Maabreh, M.; Ghaly, M.; Khan, K.; Qadir, J.; Al-Fuqaha, A. Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges. Comput. Sci. Rev. 2022, 43, 100452.
  34. Holmes, W.; Porayska-Pomsta, K.; Holstein, K.; Sutherland, E.; Baker, T.; Shum, S.B.; Santos, O.C.; Rodrigo, M.T.; Cukurova, M.; Bittencourt, I.I.; et al. Ethics of AI in education: Towards a community-wide framework. Int. J. Artif. Intell. Educ. 2022, 32, 504–526.
  35. Yang, S.J.H.; Ogata, H.; Matsui, T.; Yang, S.J.; Ogata, H.; Matsui, T.; Chen, N.-S. Human-centered Artificial Intelligence in education: Seeing the invisible through the visible. Comput. Educ. Artif. Intell. 2021, 2, 100008.
  36. Wang, Y.; Liu, C.; Tu, Y.F. Factors Affecting the Adoption of AI-Based Applications in Higher Education. Educ. Technol. Soc. 2021, 24, 116–129.
  37. Zawacki-Richter, O.; Marín, V.I.; Bond, M.; Gouverneur, F. Systematic review of research on Artificial Intelligence applications in higher education–where are the educators? Int. J. Educ. Technol. High. Educ. 2019, 16, 1–27.
  38. Sarker, I.H. Ai-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Comput. Sci. 2022, 3, 1–20.
  39. How, M.L.; Cheah, S.M.; Khor, A.C.; Chan, Y.J. Artificial Intelligence-enhanced predictive insights for advancing financial inclusion: A human-centric ai-thinking approach. Big Data Cogn. Comput. 2020, 4, 8.
  40. Rapanta, C.; Botturi, L.; Goodyear, P.; Guàrdia, L.; Koole, M. Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity. Postdigital Sci. Educ. 2020, 2, 923–945.
  41. Huang, X. Aims for cultivating students’ key competencies based on Artificial Intelligence education in China. Educ. Inf. Technol. 2021, 26, 5127–5147.
  42. Zhang, H.; Lee, I.; Ali, S.; DiPaola, D.; Cheng, Y.; Breazeal, C. Integrating Ethics and Career Futures with Technical Learning to Promote AI Literacy for Middle School Students: An Exploratory Study. Int. J. Artif. Intell. Educ. 2022, 1–35.
  43. OECD, K. OECD Science, Technology and Innovation Outlook 2018; OECD Publishing: Paris, France, 2018.
  44. Wong, T.Y.; Sabanayagam, C. Strategies to tackle the global burden of diabetic retinopathy: From epidemiology to Artificial Intelligence. Ophthalmologica 2020, 243, 9–20.
  45. Pereira, F.C.; Borysov, S.S. Machine learning fundamentals. In Mobility Patterns, Big Data and Transport Analytics; Elsevier: Amsterdam, The Netherlands, 2019; pp. 9–29.
  46. DeCoito, I.; Estaiteyeh, M. Transitioning to online teaching during the COVID-19 pandemic: An exploration of STEM teachers’ views, successes, and challenges. J. Sci. Educ. Technol. 2022, 31, 340–356.
  47. Guo, J.; Hao, J.J. Teachers’ role orientation and literacy framework in the age of intelligence. China Electron. Educ. 2021, 6, 121–127.
  48. Mäkinen, M. Digital empowerment as a process for enhancing citizens’ participation. E-Learn. Digit. Media 2006, 3, 381–395.
  49. Liu, B. Exploration on the intelligent education literacy of teachers in the era of Artificial Intelligence. Mod. Educ. Technol. 2020, 11, 12–18.
  50. Williams, T.A.; Pinto, C.A. Fundamentals of Human-Centric Artificial Intelligence (AI); COVA CCI: Williamsburg, VA, USA, 2020; pp. 3–12.
  51. Johnson, J. Artificial Intelligence & future warfare: Implications for international security. Def. Secur. Anal. 2019, 35, 147–169.
  52. Lee, H.S.; Lee, J. Applying Artificial Intelligence in physical education and future perspectives. Sustainability 2021, 13, 351.
  53. Shah, C.; Anderson, T.; Hagen, L.; Zhang, Y. An iSchool approach to data science: Human-centered, socially responsible, and context-driven. J. Assoc. Inf. Sci. Technol. 2021, 72, 793–796.
  54. Hagendorff, T. Blind spots in AI Ethics. AI and Ethics. 2022, 2, 851–867.
  55. Carabantes, M. Smart Socio-Technical Environments: A Paternalistic and Humanistic Management Proposal. Philos. Technol. 2021, 34, 1531–1544.
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