The Evolving Role of AI in Education Technology
- Published on: May 9, 2024
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- Updated on: June 20, 2024
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- Reading Time: 4 mins
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Recently, a Vienna-based platform that connects students to teachers in a virtual learning environment, raised $95 million to double down on the role of AI in its edtech product. Among its many other truly personalized learning offerings, the company aims to develop AI lesson plan generators trained on the local curriculum that can save each tutor an average of 15 minutes per lesson.
Language tutors with AI abilities are on the rise, where tutors use AI-based teaching assistants to support homework-setting and lesson planning.
Edtech companies are also scaling up their platforms to connect high-quality substitute teachers with schools in need.
As AI’s importance grows in the world of edtech, these investments underline the existing narrative: AI is the future of education technology. Reading the fledgling investment numbers may lead you to think that AI-based edtech products are all the rage among investors and the market these days.
While there is undoubtedly immense potential for AI in education, and some emerging successes, for a long-term advantage, businesses will have to do more than the competition. Which begs the question:
Are AI-Based Edtech Products Worth the Hype?
The last few years in edtech have seen the emergence of edtech products claiming to use AI. Artificial Intelligence and Machine Learning have aided the enhancements in both teaching and learning processes.
Adaptive learning platforms, intelligent tutoring systems, chatbots, and language learning assistants are some of the byproducts of the sudden uprise in the edtech sector. However, these offerings are not as advanced and functional as initially expected. And there are quite a few causes for this.
For starters, AI algorithms are known to inherit biases present in the data they are trained on and exacerbate existing inequalities and misconceptions. The lack of contextual understanding results in recommendations and feedback that are inaccurate or irrelevant to the learner’s situation.
While AI was heralded for bringing about democratization in education, AI-based edtech products often fail to cater to the digital divide among learners from different locations and backgrounds. AI products requiring large computing powers are inaccessible for users with low internet bandwidth in remote areas.
This situation is exacerbated by the resource limitations at schools and institutions. Artificial Intelligence products based on large language models requiring more resources to train and deploy are making edtech less deployable for a broad range of users. This results in a host of problems, from product-market fit to lack of funding, low ROI, and complete injunction.
How Will the Role of Artificial Intelligence in EdTech Evolve?
The examples at the outset of the blog underscore one clear message: the future role of AI in edtech lies in the innovative and agile world of nuanced edtech solutions. Imagine an AI-helping chatbot that tailors to a particular subject or assistance tools tailored to a company’s policies.
Most problems associated with AI-based edtech platforms can be fixed with a proper understanding of the target market and strategic implementation of models. For example, a large part of edtech companies are competing based on large language models supported by a high number of parameters. Take, for instance, GenAI and Open AIs’ ChatGPT which can have more than 1 trillion parameters. The cost of developing and implementing such LLM-powered tools runs high.
The counterpart to Large Language Models (LLMs) can be the application of Small Language Models (SLMs) that enable innovations at limited costs. As developers look to democratize education, SLMs empower them to implement sophisticated AI solutions using smaller datasets that serve as practical and economical solutions.
Additionally, with the advances of AI in edtech, there is an inherent challenge of safety and ethics. With advances in SLM, AI-based edtech products can become adept at both. The small data sets allow educators to moderate the content distributed through AI. At the same time, the scope of data under purview is reduced.
As edtech companies stand on the cusp of technological breakthroughs, it is important to question the assumption that it is problem-solving. This can be easily done by:
- Ensuring that there is a justified business case that considers the expected costs and outcomes of the AI product.
- Aligning the product with the ethical and sustainability standards that the community values.
By creating more nuanced and user-centric products, edtech developers will be able to create true products that balance performance and practicality for maximum deployment.
Ammar Khomusi combines his expertise as a full-stack developer and AI Engineer with a strategic consulting approach. Skilled in JavaScript, Python, React, and various other programming languages, he focuses on integrating Natural Language Processing and AI to address complex educational challenges. At Magic EdTech, Ammar has played a key role in utilizing AI to improve accessibility and inclusivity on digital learning platforms, contributing to the evolution of educational technology. His work aims to transform the educational landscape, enhancing the effectiveness and engagement of learning experiences for a diverse range of audiences.
FAQs
AI-based edtech products are attracting investors due to their potential to revolutionize the education sector by offering personalized learning experiences, improving teaching effectiveness, and addressing longstanding challenges in education delivery.
Examples include platforms like GoStudent, Preply, and Swing Education, which utilize AI to enhance various aspects of teaching and learning, such as personalized lesson planning, language tutoring, and connecting substitute teachers with schools.
AI-based edtech products may fail to deliver the expected value due to inherited biases in AI algorithms, limited contextual understanding leading to inaccurate recommendations, and accessibility issues for users in remote areas with low internet bandwidth.
AI in education can exacerbate existing inequalities and misconceptions by perpetuating biases present in the data they are trained on and by failing to address the digital divide among learners from different backgrounds and locations.
Challenges in accessibility arise from AI products requiring large computing powers, making them inaccessible for users with low internet bandwidth in remote areas, exacerbating inequalities in education access.
The future role of artificial intelligence in education involves the development of innovative and agile solutions that cater to specific educational needs, such as tailored chatbots and assistance tools, while also addressing ethical and safety concerns.
AI-based edtech products can address safety and ethical concerns by implementing Small Language Models (SLMs) that enable moderation of content by educators and reduce the scope of data under purview, thus enhancing safety and ethical standards.
Key considerations for edtech companies when developing AI-based products include ensuring a justified business case considering expected costs and outcomes, and aligning the product with ethical and sustainability standards valued by the community.
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