Generative AI Can Harm Learning in Education Systems

Generative AI can hinder learning in education systems by perpetuating misinformation. This is because AI models can produce fictional or misleading information that students may believe to be true.

Research has shown that students who rely heavily on AI-generated content may struggle to distinguish fact from fiction. For example, a study found that students who used AI to write essays often included fabricated information, which they later struggled to correct.

The consequences of this can be severe, as students may develop a distorted view of the world and struggle to think critically.

Generative AI is a subset of artificial intelligence that uses ML models to learn from data and create new content, allowing it to predict what comes next in a pattern and produce new data, images, and other forms of content.

Developers train systems and applications like DALL-E, which generates text-prompted images, and ChatGPT, which uses vast data sets, adding to the power of generative AI.

Worth a look: Generative Ai by Getty Images

GANs, which include components for creating content and evaluating its authenticity, are used to develop this technology, providing feedback for the program to improve the content.

Autoencoders enhance the way generative AI programs store and process data while reducing noise, and variations are incorporated into the encoding step to help generative applications with content creation.

Diffusion models, used by DALL-E, train on images and apply the laws of diffusion to move pixels and blur the image to a static state, allowing the program to learn by tracing the pixels back to the original image.

For your interest: Explainable Ai Generative Diffusion Models

Generative AI can be a double-edged sword in the classroom, making tasks easier for students while simultaneously deteriorating their abilities to learn the skills required to solve those tasks. This paradox was highlighted in a study that found students who used AI assistance were overly optimistic about their learning capabilities, even high-achieving students.

Teachers are struggling with students copying answers from homework, and they're worried that this will negatively impact their skill-building and fundamental understanding of concepts. This is a concern that's been echoed by researchers, who remind us that the software still has significant limitations.

Generative AI can spit out false information known as hallucinations, which can potentially harm student learning. This is a risk that's been highlighted by experts, who warn that relying too heavily on AI can lead to a lack of critical thinking and problem-solving skills.

The study also found that teachers tend to be overly concerned about the use of AI in the classroom, while students are overly optimistic. This suggests that there's a need for more education and training on how to use AI effectively to augment traditional teaching methods.

To make generative AI a valuable tool in the classroom, it's essential to approach it with a critical and nuanced perspective. This means using AI as an assistant, rather than relying solely on it for answers. By doing so, we can harness its potential to enhance learning while minimizing the risks.

Here are some potential drawbacks to consider:

Generative AI can harm learning if not implemented carefully. The potential for distributing intentionally or unintentionally harmful content, copyright issues, and data privacy concerns are among the potential cons that must be addressed.

Compromised brand reputation is a significant risk, as seen in the example of insensitive polls incorporated in news stories generated by AI. This can significantly damage a brand's reputation.

Inadequate sourcing is another issue, as generative AI can sometimes identify the sources it bases its results on, but not always. This can lead to a lack of transparency and accountability.

Inappropriate use of generative AI can also be a problem, as it's not the right choice for every situation. For example, any scenario that requires empathy or moral context or those where health and legality are on the line should have a human in charge, not AI.

Possible bias is another concern, as the information created sounds hyperrealistic, making it difficult to assess the bias coming from the source.

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Here are some of the risks and obstacles in AI education:

Additionally, there are concerns about inequality of access to this technology, security, bias in training sources, complexity and maintenance, security and data privacy, and environmental impact.

Generative AI can also lead to technological dependency and depersonalization, as seen from the perspective of faculty. It can also increase cases of plagiarism, superficial learning, and lack of critical thinking and creative skills.

The study on generative AI in education found that students who used AI assistance were overly optimistic about their learning capabilities, even the high-achieving students. Teachers, on the other hand, seem to be overly concerned and tend to dismiss the advantages of AI.

If we use generative AI lazily and completely trust the machine learning model, then that's when we could be in trouble, as Hamsa Bastani said.

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Generative AI can make tasks easier for people, but simultaneously deteriorate their abilities to learn the skills required to solve those tasks. This paradox is reflected in a study that found students who used AI assistance were overly optimistic about their learning capabilities, even the high-achieving students.

Teachers, on the other hand, seem to be overly concerned and tend to dismiss the advantages of AI. This is because students and teachers aren’t yet trained on how to use AI effectively to augment traditional teaching methods.

The study is a “cautionary tale” about deploying AI in educational settings, and it reminds everyone that the software still has significant limitations. ChatGPT, for example, is known to spit out false information known as hallucinations, which can also potentially harm student learning.

If we use AI as an assistant and do the higher-level tasks, checking its outputs and so on, it can be a huge benefit. But if we use it lazily and completely trust the machine learning model, then that’s when we could be in trouble.

Here are some potential risks of using AI in education:

These risks can be mitigated by training students and teachers on how to use AI effectively, and by using AI as a tool to augment traditional teaching methods, rather than replacing them.

Generative AI can revolutionize productivity, but it can also harm learning. This is because humans tend to rely too heavily on these tools and don't develop essential skills.

In a field experiment, nearly a thousand students used two GPT-based tutors, GPT Base and GPT Tutor. These tutors made up about 15% of the curriculum in each of three grades.

Access to GPT-4 significantly improved performance, with a 48% improvement for GPT Base and a 127% improvement for GPT Tutor. This is consistent with prior work.

However, when access to GPT-4 was taken away, students who had previously used it actually performed worse than those who never had access. This was a 17% reduction in performance for GPT Base.

Students who used GPT-4 as a "crutch" during practice problem sessions performed worse on their own.

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