Editorial
Two years on from the emergence of generative AI as a potential catalyst to disrupt education practices, certain trends seem to have appeared.
Developers of all various sizes and scales are designing and marketing tools that offer different interfaces, ways to communicate and work with large language models. Just a quick glance at an AI tool finder website, like Futurepedia, and you can see approximately 5,000 tools listed.
However, with more of an analytical approach, there are key points worth noting in how these tools are taking shape.
The 4 Categories of AI Tools
In the current context, four major categories of AI tools seem to stand out:
- Generative AI Chatbots
- Multimedia Generative AI Applications
- Generative AI Within Existing Tools/Apps
- Bespoke Applications of Generative AI
Of course, there are tools that could fit into more than one of these categories, and challenges as to classifications one user would make compared to another user. But having some form of categorization enables the following observations to be made in terms of development:
So, what does this Venn diagram tell us? Let's start by looking at the categories and the intersections.
Related Article: Issues With Scaling Generative AI in Education
1. Generative AI Chatbots
These tools have made a significant impact on learners and teachers. Ranging from free, low-end models to open source, local and premium models, generative AI chatbots are seeing an increase in all metrics: variety, speed number, features and reliability. They are the most widely used generative AI tool and are, unsurprisingly, responding to market forces accordingly.
2. Multimedia Generative AI Apps
These are apps where AI is being used to create or manipulate visual, audio and video content. In this category, we see an increase in all metrics except variety. Still, image generators have gotten significantly better at producing the quality of image or video a user requires and responding to prompts accurately.
Features are being added that enable more sophisticated use but, in terms of variety, moving into more diverse areas of multimedia, there does not appear to be much advancement, at least not in the education space.
3. Generative AI Within Existing Tools
These genAI tools highlight the trend of embedding generative AI capabilities within familiar platforms, for example: Microsoft Office suites, Google, Notion. Each day, an increasing number of these embedded tools become available. There have also been increases in reliability with baked-in AI, and in the features offered.
Some people see this as a clear way the technology will develop, given the size and reach of major software providers offering existing, mass-produced products. But it is interesting that, at present, the variety of AI in these tools and the speed at which the AI elements function does not appear to be improving.
4. Bespoke Applications of Generative AI
These apps are custom-built and developed for specific purposes, or operate in specific ways and/or to meet specific educational needs.
With a growing market, many of these are often niche tools, hence there is a wide variety. Yet they are usually from start-ups or independent developers. These foundations are likely explanations as to why these bespoke applications often do not see much in the way of added features or reliability.
The Convergence of Generative AI in Education
Looking at the diagram as an overview on generative AI developments more broadly, the overlapping areas between these categories reveal that exponential growth is still evident. And, as has been seen in very recent developments from the likes of OpenAI, the education sector is seen as a key area for these companies.
Yet, it is worth noting that the developments do not appear to be occurring in the same way for all types of tools except, of course, the increasing number of them all. It is also evident that investments are being made in different ways depending on the categories.
These are explainable in most cases, where the scale of investment/size of the provider is significant or the classification itself largely dictates what needs to happen in that space (e.g., multimedia GenAI apps producing videos are far from stable, reliable or fast at present, leading to a focus on solving those issues).
The overlapping areas also underscore the idea that generative AI in education is not developing in isolation. Instead, we are witnessing a convergence of different AI capabilities that complement one another — from chatbots becoming part of multimedia applications to bespoke tools leveraging existing platforms for customisation.
The interplay between these different types of generative AI tools highlights a shift towards a more integrated, multifaceted approach to education technology. Yet, at the same time, some more complex and separate areas of development that may affect what is available to educators, how it can be used and issues with integration into practices.
Related Article: Beyond the Textbook: AI’s Overhaul of Teaching and Learning
Generative AI’s Impact on Educational Practices
Of course, the evolving landscape of generative AI in education is more than just the addition of new tools, new features or anything related to the tools themselves; it's about how these tools are used. Still, having an understanding of how developments in these tools interact, overlap and differ can inform educators about the reality and the potential of this technology.
Trends like speed, variety, features, reliability and customization are all contributing to AI development, to how AI is perceived and used. There is potential for this technology to make educational experiences richer and more adaptable, as well as more negative. Looking at how industry is developing generative AI shows us that it is not reliant on isolated innovations but rather on how these innovations converge and sub-divide, offering the potential to build cohesive and impactful teaching and learning tools.
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For educators, understanding these trends means acknowledging both the opportunities and the challenges that come with integrating this technology into education practices. Yet, there is still a requirement for educators to engage and experiment with these tools, integrate AI into daily teaching practices and think innovatively about how this technology can meet specific educational needs.
Regardless of categories and development foci, ultimately, it's about leveraging the diversity and convergence that generative AI provides, using these tools to create more effective and engaging educational experiences.
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