Generative AI at school, work and the hospital: The risks and rewards laid bare

Generative AI has the potential to significantly transform educational settings according to the research mentioned. One of the key ways is by enhancing personal support and adaptability in learning. Chatbot tutors, for instance, are set to revolutionize educational settings by providing real-time, personalized instruction and support. This technology can enable dynamic, skill-adaptive teaching methods that respond directly to student needs without constant teacher intervention.

However, the implementation of generative AI in education must be carefully managed to avoid perpetuating or introducing biases. This pertains to the information that is fed into AIs and how they are used. For example, a study revealed that female students report using ChatGPT less frequently than their male counterparts. This disparity in technology usage could not only have immediate effects on academic achievement but also contribute to a future gender gap in the workforce.

In addition, generative AI can be used by instructors for course content creation. Instructors can use AI to create content; for example, as a first draft for course structure/syllabi, lecture structure, examples, figures, and diagrams, etc. Instructors can also generate large banks of practice problems or assessment questions, though it is important to validate any questions assigned to students for accuracy and appropriateness.

It's also important to note that while AI can assist in providing feedback for low-stakes formative assessments, it's not recommended for use in summative evaluation of student work. The integrity of the grading process is reliant on the primary role of the faculty member.

Overall, generative AI offers both opportunities and challenges in the education sector. It can help students generate and improve outlines, find and evaluate sources, provide equal access to editing suggestions, and build student confidence as they learn to critically evaluate both their own writing and that produced by AI. However, it's crucial to ensure that the use of AI in education is equitable, unbiased, and contributes positively to the learning experience.

The primary differences between generative AI and traditional AI, as described in the article, are as follows:

1. Approach: Generative AI focuses on creating new, original content, while traditional AI is primarily concerned with analyzing and processing existing data.

2. Mechanisms and Objectives: Generative AI models are trained on large datasets and use advanced algorithms, such as deep learning and neural networks, to generate novel content that can take various forms, including text, images, audio, and even code3. Traditional AI, on the other hand, relies on rule-based algorithms and pre-programmed knowledge to solve problems and is often better suited for tasks like data analysis, pattern recognition, and decision-making.

3. Adaptability: Generative AI is more adaptable to new situations and can learn from a variety of examples, while traditional AI follows predefined algorithms and rules, making it less flexible in handling unexpected scenarios.

4. Creativity: Generative AI has the ability to create new, imaginative content, which can be particularly useful in fields such as creative arts, content generation, and scientific exploration. Traditional AI, in contrast, excels at tasks that require precise, logical reasoning and decision-making but lacks the flexibility and adaptability of generative AI.

5. Resource Intensity: Traditional AI generally requires less computational resources for training compared to generative AI, which can be more resource-intensive, especially in complex tasks, due to the demands of learning from diverse data and generating creative outputs.

6. Problem-solving Approach: Traditional AI follows predefined algorithms for problem-solving, while generative AI utilizes learned patterns for problem-solving, allowing it to find creative solutions to challenges.

7. Application Domains: Traditional AI is commonly used in rule-based tasks like data analysis and decision-making, while generative AI excels in creative tasks like image generation, music composition, and language generation.

8. Human Interaction: Traditional AI may lack a natural interface and interact based on programmed responses, while generative AI can offer more natural and human-like interactions, understanding and generating content in a way that feels conversational.

9. Uncertainty Handling: Traditional AI struggles with uncertainty and ambiguity, as it prefers clear rules, while generative AI is better at handling uncertainty, making it more adaptable to situations with incomplete or unclear information.