K12 education in the year 2030

Matt Poole

Royal Roads University

LRNT523 Foundations of Learning and Technologies

Dr. Elizabeth Childs

October 29, 2023

K12 education in the year 2030

Education raises peoples’ productivity, income, gives them more control over their lives, health, and benefits all of society (World Bank, 2018).  The unforeseen Coronavirus pandemic accelerated online education adoption when schools around the globe were forced to close their campuses.  While it has been a mixed-bag of results, one theme that has emerged is a call to radically reimagine education as flexible and relational, not only for the sake of the individual learner, but to avoid systemic dystopian futures (Veletsianos and Houlden, 2020; Costello et al., 2020; Selwyn et al., 2020).  Flexible education is not a structure limited to accessibility issues and pedagogical methods, but is rather a mindset of malleability and adaptation (Veletsianos and Houlden, 2020). Relational education can be viewed through the lens of Social-Emotional Learning (SEL) where an individual’s wellbeing, physical environment and emotional state, deeply affect their learning.  In order to extrapolate what that could mean for the future, this paper draws inspiration from 25 Years of EdTech (Weller, 2020)  and looks back on the recent past to identify trends and innovations that will help shape the future; and in conclusion I will make five bold predictions for K12 education in the year 2030.  

SEL can be traced back to Yale University in the year 1968, and studies on its merits began in the 1990s after the Collaborative for Academic, Social, and Emotional Learning (CASEL) was founded in 1994 and formally defined the field.  The tenets of SEL are about the awareness and management of an individual’s emotions, effective problem solving, building positive relationships and managing challenging situations capably (Zins and Elias, 2007).  It is a departure from rote memorization with an emphasis on continual practice.  SEL has now been extensively studied across a range of social, economic and cultural contexts. The findings consistently identify three main benefits for students: improved well being, higher academic achievement, and better health choices (OECD, 2021).  

In the late 2010s numerous studies found that Computer Assisted Learning (CAL) improved test scores, increased students’ interest in learning, (Furman et al., 2019;  Muralidharan et al., 2019; Lai et al., 2015; San and Aykac, 2020; Kelly and Rutherford, 2017) and sometimes out-performed the human teacher altogether (Bettinger et al., 2020; Buchel et al., 2020), leading to questions of whether computers could (or should) replace human teachers.  Proponents of such a radical change note that computers can more effectively plan lessons, deliver assessment, and predict student retention (Cui et al., 2022; Ausat et al., 2023).  Economic considerations aside, it seems like there is an opportunity to leverage this on a greater scale.

Around the same time, large language models (LLMs) began training, giving rise to artificial intelligence (AI).  The initial reaction to generative AIs varied greatly depending on the context.  In the field of education, only 52% of people viewed it positively, while 32% viewed it negatively, and 16% remained neutral (Haque et al., 2022).  The positive sentiment centered around opportunities for learning.  LLMs helped children develop reading and writing skills, critical thinking and comprehension skills, and assisted in teaching curriculum with step-by-step instruction for problem solving (Kasneci et al., 2023).  Much of the negative sentiment stemmed from uncertainty and doubt as early iterations would plagiarize essays (Khalil and Er, 2023), provide false or misleading information (Tlili et al., 2023), and perpetuate existing biases of discrimination (Kooli, 2023).  The misrepresentation of information was a two-way street where humans would attribute falsified, overstated, or unsubstantiated claims to the power of AIs (Raji et al., 2022) and science fiction authors wrote about how AI wouldn’t need humans and kill us all (Russel, 2019).  Fortunately LLMs are trained on specific data sets and are not conscious entities.

In 2023, Khan Academy launched Khanmigo, a chatbot service offering every student a personal tutor and every teacher an assistant.  Students can ask “why do I need to learn this?” and the AI will make a connection to something the student is interested in.  It also lets students interact with historic and fictional characters; where the AI will answer as if it were that character, allowing for a deeper understanding of literary themes and historical contexts.  Teachers employ Khanmigo to assist with lesson plan generation, report writing, and student assessment, freeing up time to develop and deepen healthy relationships with students (Khan Academy, 2023).    

Finally, much attention in education has recently been drawn to the “digital divide” meaning inequalities around access to digital devices and content.  There are several ways in which the digital divide can be conceptualized, and different approaches lead researchers to emphasize different aspects of the problem (Yu et al., 2018).  It is viewed as a socio-econmic issue perpetuating a gap between developed and developing countries on a global scale, and a demographics gap within populations.  The numbers are alarming but do not always paint an accurate picture.  According to the UN, two thirds of school age children do not have internet access at home (UN, 2020).  Inevitably some will not, but that does not mean these 1.3 Billion children do not have access to the internet, it simply means they do not have a router at home.  Similarly, when the Mayor’s Office in New York City published a report stating that one-in-four homes do not have internet access (NYC.gov, 2020), they are not taking into account that seniors, prisoners and low-income earners are often accessing the internet elsewhere.  

Rather than one universal vision of K12 education within the context of the current system for the year 2030, I offer a few general predictions.  First, in an age where AI has replaced most analysis jobs, I expect that teachers will spend most of their time as supervisors, trainers, sages, and interventionists; leaving the lesson planning, dissemination of curriculum, and assessment of students in the capable hands of AI.  I further predict there will be a myriad of AI teachers to choose from, much like we have generalist and specialist teachers, and multiple content platforms for the same medium, we can expect the same for AIs.  Second, AIs will teach for mastery of concepts, ideas, and execution before allowing students to progress to the next level of achievement and as a result student demographics will start to blend in relation to their level of achievement rather than separate students by age, although for the average student it stands to reason that most of their peers in any given class will be of a similar biological age.  Third, I predict a greater emphasis on SEL at school, both in and out of the classroom, particularly in group environments.  AIs will use facial recognition and biometrics to detect when students are struggling and call for human support to assist when automated interventions fail.  This is not to be confused with an Orwellian oversight, but rather having effective diagnostic tools to identify deviations from the individual’s baseline in a holistic manner.  Fourth, with a greater emphasis on the individual and flexibility, the daily schedule will unfold over a longer (or shorter) period of time, as opposed to the standard 8-hours a day, 40 hours a week.  Since students will be able to access their AI teachers on demand, there will no longer be a requirement to study at suboptimal times.  Finally, in an effort to narrow the global digital divide, I expect more schools to be the primary point of internet access for many students, especially within under-served communities, potentially leading to an increase of boarding schools where students can be connected 24/7.

References

Alam, A. (2021). Should Robots Replace Teachers? Mobilisation of AI and Learning Analytics in Education. 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3), 1–12. https://doi.org/10.1109/ICAC353642.2021.9697300

Ausat, A. M. A., Massang, B., Efendi, M., Nofirman, N., & Riady, Y. (2023). Can Chat GPT Replace the Role of the Teacher in the Classroom: A Fundamental Analysis. Journal on Education, 5(4), 16100–16106. https://doi.org/10.31004/joe.v5i4.2745

Bettinger, E., Fairlie, R., Kapuza, A., Kardanova, E., Loyalka, P., & Zakharov, A. (2020). Diminishing Marginal Returns to Computer-Assisted Learning (w26967; p. w26967). National Bureau of Economic Research. https://doi.org/10.3386/w26967

Bozkurt, A., Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., Lambert, S. R., Al-Freih, M., Pete, J., Olcott Jr., D., Rodes, V., Aranciaga, I., Bali, M., Alvarez Jr., A. V., Roberts, J., Pazurek, A., Raffaghelli, J. E., Panagiotou, N., de Coëtlogon, P., … Paskevicius, M. (2020). A global outlook to the interruption of education due to COVID-19 Pandemic: Navigating in a time of uncertainty and crisis. Asian Journal of Distance Education, 15(1), 1–126. https://doi.org/10.5281/zenodo.3878572

Büchel, K., Jakob, M., Kühnhanss, C., Steffen, D., & Brunetti, A. (2020). The Relative Effectiveness of Teachers and Learning Software: Evidence from a Field Experiment in El Salvador. University of Bern Social Sciences Working Papers. https://ideas.repec.org//p/bss/wpaper/36.html

Costello, E., Brown, M., Donlon, E., & Girme, P. (2020). ‘The Pandemic Will Not be on Zoom’: A Retrospective from the Year 2050. Postdigital Science and Education, 2(3), 619–627. https://doi.org/10.1007/s42438-020-00150-3

Cui, Y., Song, X., Hu, Q., Li, Y., Sharma, P., & Khapre, S. (2022). Human-robot interaction in higher education for predicting student engagement. Computers and Electrical Engineering, 99, 107827. https://doi.org/10.1016/j.compeleceng.2022.107827

Ferman, B., Finamor, L., & Lima, L. (2019, June 27). Are Public Schools in Developing Countries Ready to Integrate EdTech into Regular Instruction? [MPRA Paper]. https://mpra.ub.uni-muenchen.de/109063/

Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022). “I think this is the most disruptive technology”: Exploring Sentiments of ChatGPT Early Adopters using Twitter Data (arXiv:2212.05856). arXiv. https://doi.org/10.48550/arXiv.2212.05856

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Kelly, D. P., & Rutherford, T. (2017). Khan Academy as Supplemental Instruction: A Controlled Study of a Computer-Based Mathematics Intervention. The International Review of Research in Open and Distributed Learning, 18(4). https://doi.org/10.19173/irrodl.v18i4.2984

Khalil, M., & Er, E. (2023). Will ChatGPT get you caught? Rethinking of Plagiarism Detection (arXiv:2302.04335). arXiv. https://doi.org/10.48550/arXiv.2302.04335

Khanmigo Education AI Guide. (n.d.). Khan Academy. Retrieved October 29, 2023, from https://www.khanacademy.org/khan-labs

Kooli, C. (2023). Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions. Sustainability, 15(7), 5614. https://doi.org/10.3390/su15075614

Lai, F., Luo, R., Zhang, L., Huang, X., & Rozelle, S. (2015). Does computer-assisted learning improve learning outcomes? Evidence from a randomized experiment in migrant schools in Beijing. Economics of Education Review, 47, 34–48. https://doi.org/10.1016/j.econedurev.2015.03.005

Muralidharan, K., Singh, A., & Ganimian, A. J. (2019). Disrupting Education? Experimental Evidence on Technology-Aided Instruction in India. American Economic Review, 109(4), 1426–1460. https://doi.org/10.1257/aer.20171112

Nations, U. (n.d.). Universal Declaration of Human Rights. United Nations. Retrieved October 28, 2023, from https://www.un.org/en/about-us/universal-declaration-of-human-rights

NYC.gov (Jan, 2020) The New York City internet master plan.  Retrieved October 28, 2023, from http://www.nyc.gov/assets/cto/downloads/internet-master-plan/NYC_IMP_1.7.20_FINAL-2.pdf

OECD. (2021). Beyond Academic Learning: First Results from the Survey of Social and Emotional Skills 2019. OECD. https://doi.org/10.1787/92a11084-en

Raji, I. D., Kumar, I. E., Horowitz, A., & Selbst, A. (2022). The Fallacy of AI Functionality. 2022 ACM Conference on Fairness, Accountability, and Transparency, 959–972. https://doi.org/10.1145/3531146.3533158

Rao, A., Hilton Iii, J., & Harper, S. (2017). Khan Academy Videos in Chinese: A Case Study in OER Revision. The International Review of Research in Open and Distributed Learning, 18(5). https://doi.org/10.19173/irrodl.v18i5.3086

Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.

Şan, İ., & Aykaç, T. (2020). Effect of khan academy-aided teaching on academic achievement in English course. Cypriot Journal of Educational Sciences, 15(5), 1107–1116. https://doi.org/10.18844/cjes.v15i5.5174

Selwyn, N., Pangrazio, L., Nemorin, S., & Perrotta, C. (2020). What might the school of 2030 be like? An exercise in social science fiction. Learning, Media and Technology, 45(1), 90–106. https://doi.org/10.1080/17439884.2020.1694944

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x

Veletsianos, G., & Houlden, S. (2020). Radical Flexibility and Relationality as Responses to Education in Times of Crisis. Postdigital Science and Education, 2(3), 849–862. https://doi.org/10.1007/s42438-020-00196-3

Weller, M. (2020, February). 25 Years of Ed Tech. AU Press—Digital Publications. https://read.aupress.ca/projects/25-years-of-ed-tech  

World Development Report 2018: Learning to Realize Education’s Promise. (n.d.). [Text/HTML]. World Bank. Retrieved October 28, 2023, from https://www.worldbank.org/en/publication/wdr2018

Yu, B., Ndumu, A., Mon, L. M., & Fan, Z. (2018). E-inclusion or digital divide: an integrated model of digital inequality. Journal of Documentation, 74(3), 552–574. https://doi.org/10.1108/JD-10-2017-0148

Zins, J. E., & Elias, M. J. (2007). Social and Emotional Learning: Promoting the Development of All Students. Journal of Educational and Psychological Consultation, 17(2–3), 233–255. https://doi.org/10.1080/10474410701413152