AI-powered curriculum development: What are the pros and cons research has revealed?

Contents:

• What are the risks associated with using AI in designing educational programs?
• What benefits does AI bring to learning design?
• What should be considered when implementing AI in learning design?

In the past two years, many scientific studies have been conducted on the impact of generative AI on the educational process. Philippa Hardman, a former research fellow at the University of Cambridge and creator of the DOMS™️ learning design system, summarized the key findings of these studies. She highlighted both the objective risks and benefits associated with integrating AI into instructional design. These findings highlight the importance of a conscious approach to implementing technologies in educational practices and the need to adapt them to modern requirements. In this article, we will review the observations from the studies selected by Philippa, focusing on the less obvious advantages and disadvantages of neural networks in the context of educational program design and development of learning materials. We will not delve into well-known issues such as hallucinations, information security risks associated with using open-source neural networks, or ethical and legal issues. Instead, our goal is to analyze how neural networks can influence the educational process and what aspects should be considered when implementing them.

What are the risks associated with using AI in educational program design?

Recent studies have identified vulnerabilities of artificial intelligence that must be considered when developing educational systems based on neural networks. These weaknesses can affect the effectiveness of training and the quality of AI performance. The use of neural networks for educational purposes requires careful analysis and optimization to minimize risks and improve effectiveness. When designing such systems, it is important to pay attention to both the technical aspects and the ethical issues associated with the use of artificial intelligence.

The use of AI tools in the educational process can significantly improve the efficiency of educational design and facilitate its scalability. However, researchers emphasize that AI tools that operate on ready-made templates significantly limit the ability of instructional designers to make independent methodological decisions. This challenges the flexibility and adaptability of educational programs, which can negatively impact learning quality. It's important to find a balance between automation and individualized approaches in instructional design to achieve the best results. Research conducted by researchers from Norway and the UK has shown that tools based on rigid frameworks that standardize the instructional design process limit the freedom of instructional designers. This creates a feeling of being "locked in" and complicates the adaptation of educational materials to the needs of the target audience. At the same time, more flexible solutions exist on the market, such as iLUKS and ChatCLD. These tools offer a structure that serves as a guide for instructional designers, but also allow for the freedom to refine, clarify, and supplement various elements of the educational program. The flexibility of such tools facilitates more efficient and targeted creation of educational materials, which is essential for successful learning.

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Comparing Course Development Approaches: Instructional Designers with Neural Networks vs. Without Ones. The use of neural network technologies is becoming increasingly popular in modern education. The question is whether an instructional designer, relying on the capabilities of a neural network, can create a higher-quality and more effective course compared to someone working without its help. Neural networks can accelerate the process of data analysis, content adaptation, and personalization of learning, which in turn can lead to the creation of more interactive and engaging educational materials. However, the human factor, creativity, and intuition of the instructional designer remain important aspects in course development. What are the real advantages and disadvantages of both approaches? This will provide a deeper understanding of how to optimize the process of creating educational content in the context of modern technology.

Research shows that artificial intelligence often generates content without taking into account methodological principles and contextual specifics. For example, a study from China demonstrated that mathematics lesson plans developed using the GPT-4 model required significant adjustments in 78% of cases to meet educational standards and student levels. This highlights the importance of manually refining AI-generated materials to ensure their quality and compliance with educational requirements.

Belgian researchers have found that AI tools in instructional design tend to mimic AI principles rather than apply them to deeply analyze and refine content. According to the researchers, many instructional design professionals follow AI recommendations without critical evaluation, which negatively impacts the quality of the final product. This underscores the importance of a conscious approach to using AI in educational processes and the need to combine technology with pedagogical experience to achieve effective results.

Neural networks can be used to generate initial content that must be carefully checked for compliance with instructional design principles. Such content requires significant revision. Therefore, the time savings anticipated by users may be questionable or insignificant given the time spent checking and revising materials.

Philippa Hardman notes that research into the work of instructional designers demonstrates a variety of opinions regarding the role of creativity in their work. On the one hand, AI-based tools can significantly aid in idea generation. On the other hand, there is a risk of automation and standardization of all processes. If specialists begin to rely excessively on neural networks when making creative decisions, this can negatively impact the quality of the final result. It is important to find a balance between the use of technology and maintaining an individual approach to creative work.

Research conducted in the United States has shown that instructional designers who actively use artificial intelligence in their work are less likely to create original assignments for students compared to their colleagues who are less interested in neural networks. Similar conclusions were reached in a study from Belgium, which also noted that an uncritical attitude to AI suggestions leads to the creation of educational materials that become monotonous and banal. This emphasizes the importance of critical thinking and a creative approach in the educational process, especially in an era of the active integration of technology into learning.

Content generation using neural networks can be a suitable solution for creating standard and templated educational assignments and materials. However, if you strive for originality, it is important to use neural networks wisely. They can be a great resource for brainstorming with your team, but final ideas and concepts should be refined independently. We'll explore how to effectively integrate neural networks into the learning content creation process later.

Using neural networks, you can generate initial text that requires detailed review for compliance with instructional design principles. This process requires significant revision effort, making the real time savings questionable. Given the need for review and editing, the resulting savings may be negligible.

How AI Benefits Learning Design

Recent research has identified key areas where generative artificial intelligence can significantly improve instructional design. Surprisingly, many of these areas also come with certain risks. The reasons for this lie either in the inconsistent findings of the studies themselves or in the ways these tools are used. It's important to understand that to achieve maximum effectiveness, generative AI implementation must be carefully considered, considering both its advantages and potential drawbacks.

Artificial intelligence significantly accelerates the development of educational materials. Research conducted in Korea shows that using ChatGPT can reduce the time spent on lesson planning by 65%. Meanwhile, Chinese researchers have developed TreeQuestion, an AI platform for generating multiple-choice test questions, which reduces test creation time by 95%. These achievements highlight the potential of AI in education, making the learning process more effective and accessible.

We are aware of these risks and understand that reducing the time for creating educational content can negatively affect on its quality, unless we check and edit materials generated by artificial intelligence. If we devote time to checking and editing, the question arises about the real effectiveness of the process: how significantly is the time for preparing materials reduced, taking into account these additional costs.

Artificial intelligence significantly simplifies and scales the process of assessing student knowledge and skills. Automated test creation saves teachers time and allows for more regular assessments, which, according to a study from China, does not reduce the accuracy of the results. To generate high-quality tests, researchers recommend using Bloom's Taxonomy in prompts. However, it is important for teachers to remember that questions generated by a neural network must be appropriate for the level of students and adequately measure the achievement of specific learning goals. Thus, the combination of technology and pedagogical supervision can significantly improve the effectiveness of educational assessment.

Personalized learning is an approach in which the educational process is customized to the student's level of knowledge, experience, interests, and preferences regarding the pace and methods of information acquisition. Contextualization of learning is a current trend in learning and development (L&D), which also involves adapting curricula but with a focus on the specifics of the professional environment. This means that teaching materials and methods are tailored to the specific context of students' work, improving knowledge acquisition and practical application. Education experts emphasize that artificial intelligence (AI) opens up new possibilities for adaptive learning, tailored to the individual learner's level. Recent research supports this view. For example, the use of chatbots tailored to different student groups using the persona method resulted in a 29% increase in learning outcomes in a course on information processing. Experts attribute this effect to the fact that adapted explanations, prompts, and assignments facilitate more effective learning than standard educational content. Adaptive technologies based on AI can significantly improve the quality of education by offering a personalized approach to learning and increasing student engagement.

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Personalization using neural networks Currently, this seems like a complex task. However, within a year, this technology will become available and significantly simplify the process of adapting content to user needs. Neural networks open up new possibilities for improving the user experience, and their integration into a personalization system will help create more targeted and relevant offers. In the near future, we will see significant progress in this area, allowing businesses to more effectively engage with customers and increase satisfaction levels.

There is a common belief that artificial intelligence is incapable of adapting materials to a specific context. However, this statement is not entirely true and depends on the settings and degree of human involvement in the process. For example, a group of US researchers developed the ARCHED framework for designing student-centered adaptive learning. This framework includes two AI-powered tools: one generates educational content, and the other evaluates it for compliance with the principles of instructional design and learning objectives defined in Bloom's taxonomy. A human being controls the entire process, adjusting parameters and making methodological decisions to adapt the program to different groups of students. Thus, AI can effectively support the educational process while remaining under the teacher's control.

Philippa Hardman argues that numerous studies confirm the ability of AI chatbots and adaptive learning systems to adapt to student needs in real time. These technologies can analyze student actions and their current performance, providing feedback and suggesting relevant learning content and tasks that match the level of complexity required for effective learning.

The question of the risks of implementing neural networks often includes the assertion that they can limit creativity. However, Philip Hardman emphasizes that everything depends on how specialists use AI tools. With the right approach, neural networks can become effective creative assistants, capable of generating ideas that a human being might not be able to come up with on their own. Proper use of AI technologies opens new horizons for creativity and innovation, allowing professionals to expand their capabilities and find innovative solutions. Research conducted in the United States shows that brainstorming using AI generates 47% more diverse ideas compared to traditional brainstorming with a human team. Additionally, the study found that ChatGPT can offer educators a wide range of content delivery methods and learning activity formats, significantly enriching the educational process. The use of AI in education opens new horizons for creating effective and innovative approaches to learning.

It is important to remember that the use of neural network suggestions should not be direct and unthinking. This can negatively impact the depth and diversity of students' learning experiences. Artificial intelligence is particularly useful in the initial stages of course design. In this context, instructional designers can use AI to generate ideas for new courses, assignment suggestions, and outlines of learning materials. Research shows that such tools save time and free up cognitive resources, which makes it possible to more thoroughly evaluate the ideas received, select the most suitable ones, refine them, integrate them into a unified concept, and adapt them to a specific learning context. The use of AI in the educational process opens new horizons for creating high-quality and effective learning.

What to consider when implementing AI in instructional design

Philippa Hardman argues that current research shows that expectations about the revolutionary impact of artificial intelligence on instructional design are often exaggerated. Nevertheless, the advantages that neural networks can bring to this field cannot be ignored. Artificial intelligence effectively tackles certain tasks and demonstrates significant results, while highlighting important aspects where human input remains indispensable. This highlights the need for a balanced approach to integrating AI into educational processes, taking into account both its capabilities and limitations.

To effectively leverage the benefits of artificial intelligence in instructional design and minimize risks that could negatively impact learning quality, Hardman offers the following recommendations. First and foremost, it's crucial to integrate AI into the learning process, ensuring it supports and complements, rather than replaces, traditional teaching methods. It's also crucial to carefully monitor the quality of the data used by AI to avoid biased results. Furthermore, regular evaluations and testing should be conducted to ensure that AI implementation truly improves learning outcomes. It's also crucial to train faculty and students in the basics of using AI to maximize its potential and minimize potential drawbacks. Instructional designers need to stay abreast of new AI trends and technologies to adapt their methods and approaches to the changing educational environment.

• Develop digital literacy and prompt engineering skills—well-written prompts improve the quality of generation by 58% compared to basic ones.
• Avoid AI tools that severely limit the instructional designer's independence. Instead, choose flexible solutions that allow for refinement and adaptation of neural network output.
• Entrust AI with generating drafts and versions, analyzing large amounts of data, scaling feedback, adapting content for different student groups, and other routine tasks that should be automated. And devote the saved time to making creative, methodological, and strategic decisions, which neural networks, unlike humans, are not strong in.
• Carefully monitor the quality of generated materials and never use them "as is." It is important to ensure not only that there are no “hallucinations,” but also that the content corresponds to the methodological principles, educational goals, characteristics of the target audience, and the learning context.
• Pay attention to the diversity of educational content, avoid excessive standardization.

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