Artificial intelligence (AI)—and its subset, generative AI—are here not only to stay, but to grow in use in prominence in all fields, including education. The debate of traditional AI versus generative AI is not about which is better, but how each serves purposes and functions within the educational landscape.

AI is used in education in numerous ways, including:

• customizing learning materials to fit a student’s learning style;
• automating administrative tasks such as grading and scheduling, freeing up time for educators to focus on teaching;
• provide grammar and style instructions to improve writing skills; and
• analyzing large amounts of student data to help policymakers make informed decisions.

A few of the ways that generative AI can aid teachers and learners is by:

• analyzing existing curricula and suggesting updates, new topics, and interdisciplinary learning opportunities;
• converting learning resources into more accessible formats, such as audio books, simplified text, and interactive tools;
• creating interactive scenarios to help students practice language skills; and
• creating synthetic data sets to protect student privacy.

Here, we’ll look at the differences between traditional AI and generative AI, explore how traditional AI and generative AI work and what their challenges and limitations are, and envision the future of education in light of the impact of traditional AI and generative AI on the field.

What is the Difference Between Traditional AI and Generative AI?

It’s not an either-or choice between the types of AI; each has its strengths and uses and can work together. We’ve noted some of the ways each can be used in education. Now let’s look at some of the key differences.

In terms of generative AI vs. general AI, know that the two have different goals and purposes. People use generative AI to create new content. Traditional AI’s uses are broader and deeper, and open to whatever the algorithm coder’s focus is: on better decision-making, on spotting anomalies, or on cybersecurity issues, for example.

What is the difference between generative AI and AI? Here are some of the common differences:

Focus

AI analyzes and interprets existing data to improve efficiency, accuracy, and decision-making within predefined boundaries. Generative AI creates text, images, music, and models based on existing data.

Uses

AI is used in predictive analytics, natural language processing, and autonomous systems. Generative AI is used to generate information—thus, it is used in content creation and design and in scientific research, where scientists are creating new hypotheses and models.

Transparency

Generative AI models often function as “black boxes,” rendering their decision-making processes less transparent than traditional AI models, which are typically more transparent and interpretable.

Performance and Efficiency

Generative AI models often require substantial computational resources and training time, making it more difficult to scale compared to traditional AI, which is typically more efficient—particularly when performing well-defined tasks that require less extensive model training.

Data Requirements

Generative vs. non-generative AI systems have different data needs; generative AI requires larger datasets, while traditional AI can operate effectively with smaller datasets, depending on the complexity of the task and the model used.

Adaptability

Traditional AI is adaptable to some degree, but often needs specific training for each unique task or application. Generative AI can adapt to various domains and generate content across different fields.

Understanding Artificial Intelligence

The term artificial intelligence was first used in 1956. In the 1960s, the US Department of Defense began training computers to mimic human reasoning. The Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. DARPA created intelligent personal assistants in 2003, long before Siri, Alexa, or Cortana existed.

This early work paved the way for the automation and formal reasoning capabilities that computers possess today. AI has evolved to provide many specific benefits in every industry, including education.

In this section, we’ll explain what AI is, how it works, what its benefits and uses are in education, challenges and limitations associated with AI, and some uses of it in various fields.

What is AI?

AI is a set of technologies that enables machines to mimic human intelligence and perform advanced functions. It can:

• understand and respond to spoken and written language;
• analyze data and make recommendations;
• learn from new information and make decisions;
• use math and logic to simulate human reasoning; and
• solve complex problems by perceiving its environment, dealing with what it perceives, and acting to achieve a goal.

How AI Works

AI uses algorithms, data, and computational power to simulate human intelligence and enable machines to perform tasks that typically require human thought. AI systems work by collecting large amounts of data and applying mathematical models—algorithms—to the data. From there, AI systems learn from the patterns observed in the data and make predictions based on those patterns. That pattern recognition goes on continuously, with the AI system adapting its predictions as more data is collected and analyzed. The systems test and measure their own performance and develop additional expertise each time they process data.

Uses and Benefits of AI in Education

Artificial intelligence has numerous uses in education, including:

• Personalized learning: AI can analyze student performance and preferences to create customized lesson plans and assessments. 
• Automated grading: AI can score students as they take tests, reducing the burden of grading for teachers. 
• Chatbots: Chatbots can help students learn specific subjects, such as math or reading comprehension. 
• Tutoring: AI can provide tutoring outside of the classroom through chatbots or other tools. 
• Admissions: AI can help higher education institutions improve the quality and speed of the admissions process. 
• Study partners: AI can help students find study partners with similar interests. 
• Video learning: AI can create personalized video learning experiences. 
• Educational games and simulations: AI can teach through educational games and simulations. 
• Early intervention: AI can analyze student performance to help with early intervention. 
• Administrative tasks: AI can automate administrative tasks.

Challenges and Limitations

For all of its great uses, AI also has some challenges and limitations. For example, AI systems require large amounts of high-quality data to function effectively, and obtaining this data can be challenging. AI models can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes.

In addition, many AI models, especially deep learning, are complex and lack transparency, making it difficult to understand how they make decisions. AI systems can be vulnerable to attacks and misuse, and handling sensitive data raises significant privacy concerns. Developing and maintaining AI systems can be expensive and resource-intensive, requiring specialized expertise and infrastructure.

Finally, the use of AI raises ethical issues, including job displacement, surveillance, and the potential for autonomous systems to make harmful decisions. AI models can struggle to generalize from training data to new, unseen situations, limiting their adaptability.

Use Cases

Let’s take a brief look at how AI is being used in different fields.

We’ve already talked about its uses in education, where, among other things, AI supports personalized learning, automated grading, administrative tasks, and learning analytics.

In healthcare, AI is used for diagnosing diseases, personalized treatment plans, drug discovery, and managing patient data.

The field of finance uses AI in fraud detection, risk management, algorithmic trading, and personalized financial advice.

AI powers retail through recommendation engines, inventory management, customer service chatbots, and personalized marketing.

AI enables autonomous vehicles, traffic management systems, route optimization, and predictive maintenance in the field of transportation.

The entertainment field uses AI for content recommendations, game development, video and music streaming services, and content creation.

AI streamlines human resources recruitment processes, employee engagement, performance analysis, and talent management.

Exploring Generative AI

Generative AI—a type of AI that can create new content and ideas based on prompts, using machine learning models to learn patterns from large amounts of data—was introduced in the 1960s in chatbots. But it wasn’t until 2014 that generative AI, using generative adversarial networks, or GANS, could create convincingly authentic images, videos, and audio of real people.

In this section, we’ll detail what generative AI is, explore how it works, discuss its benefits, outline its challenges and limitations, and point out uses of it in various fields, including education.

What is Generative AI?

Generative AI is a type of AI that uses deep learning techniques to create new content, such as images, music, animation, 3D models, and text.

There are many types of deep learning models used in generative AI, including GPT-n (ChatGPT is a chat application based on the GPT models), the aforementioned generative adversarial networks, and recurrent neural networks, a deep learning algorithm that recognizes patterns in data sequences such as natural language or time series. This list is not exhaustive.

Generative AI can take raw data—say, all of Wikipedia or the collected works of Rembrandt—and “learn” to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that’s similar, but not identical, to the original data.

How Generative AI Works

Generative AI models are trained on large datasets that contain examples of the content they will generate. They learn to recognize patterns and features within this data and develops an understanding of the underlying structure. Once trained, a model can generate new, original content that mirrors the characteristics of the data it has seen before.

Generative AI often uses advanced neural network architectures, such as the previously mentioned generative adversarial networks (GANs) or variational autoencoders (VAEs). GANs consist of a generator and a discriminator. The generator creates new content, while the discriminator evaluates its authenticity. The two networks compete, improving each other over time. VAEs encode input data into a compressed latent space and then decode it to generate new, similar data.

The quality of the generated content is evaluated, and feedback is used to refine the model and improve its output to meet specific requirements or preferences

Uses and Benefits of Generative AI in Education

Generative AI has many uses and benefits in education. Many teachers are using generative AI tools to quickly create lesson plans, curriculum unit summaries, student assessment rubrics, class discussion topics, learning resources, classroom activities, and much more.

Beyond those uses, teachers can employ generative AI to personalize learning experiences for students, tailoring instruction and feedback to meet each student’s unique needs and strengths; create content—e.g., text, images, music, and videos—to enhance creativity and enable the production of unique materials; and create synthetic data to augment training datasets, improving the performance of other AI models.

Other uses of generative AI in the classroom include generating realistic simulations for training, testing, and research in various fields; improving natural language processing applications, such as chatbots and virtual assistants, by generating more natural and coherent responses; and creating assistive technologies, such as automatic text generation for people with disabilities, making information more accessible.

Challenges and Limitations

While the benefits of using generative AI in the classroom run both deep and wide, the technology is not without its concerns. Some areas of concern include:

• Quality control: Ensuring the quality and accuracy of the content generated by AI can be challenging, as it may produce misleading or incorrect information. It can also lead to students cheating.
• Ethical concerns: Generative AI raises ethical issues, such as the creation of deep fakes, the potential for misuse in spreading misinformation, and intellectual property concerns.
• Bias and Fairness: Generative AI models can inherit biases from training data, leading to biased or unfair outputs.
• Transparency and Explainability: The decision-making process of generative AI models is often complex and opaque, making it difficult to understand how outputs are generated.
• Resource intensive: Training and running generative AI models can be computationally expensive and require significant resources.
• Security and Privacy: Generative AI can be used to create realistic but fake content, posing security risks and raising privacy concerns.
• Generalization issues: Generative AI models can struggle to generalize beyond their training data, leading to outputs that are not relevant or accurate in new contexts.

Use Cases

The applications of generative AI to essentially any and every field is only limited by the creativity of those applying it. As we’ve noted, in education, generative AI can be used to personalize educational content, create simulations for interactive learning, and assist in language learning by generating practice materials (among its many others uses in the classroom).

Generative AI can also be used in content creation and art and design—to create articles, blog posts, marketing materials, and social media content, and to generate digital art, design products, and develop new styles.

In healthcare, generative AI is used to create synthetic medical data for research, generate patient-specific treatment plans, and assist in medical image analysis. In entertainment, the technology allows for the creation of video game content, scripts for movies and TV shows, and the generation of music and soundtracks. In marketing, generative AI creates targeted marketing campaigns, generates product descriptions, and personalized advertisements.

And that’s truly the tip of the iceberg for how generative AI can accelerate the creation and production of materials and for all its other uses in a wide variety of fields.

The Future of Education

The future of education and the future of AI are inextricably intertwined. AI is truly changing the face of education with its tools for educators, administrators, and students. Learning will steer away from a one-size-fits-all system and lean into an AI-enabled experience that is self-paced, self-directed, deeply responsive, and hyper personal to each learner’s needs.

The use of AI systems will cause educators from K through graduate school to reconsider ways to ensure that students are retaining material, learning critical life skills, and understanding how to apply those skills to careers that will no doubt be deeply impacted by AI. 

 Educators will need to create new forms of tailored educational content and provide AI-enhanced capabilities to make sure students are deeply engaged with the material and can apply that learning, as well as ensure that students are mastering critical AI skills that they can use in the future.

AI has presented educators across the globe with the complex and vital task of determining how to maximize the benefits of AI while minimizing its risks and limitations. Learning to corral and most effectively use AI in the classroom is one of the greatest challenges—and opportunities—that the field of education faces today. How the field responds will shape the future of education, of learners, and ultimately, of life as we know it.

College of Education Offerings

The College of Education keeps on top of all the issues related to traditional and generative AI. For other articles on AI’s influence on education, see How Will AI Power New Learning in Education, Institute Uses AI to Promote Equity in STEM Education, and Using AI to Improve Education and Impact Society.

Interested in how you can integrate AI into your work? We offer several online graduate degree programs with concentrations in Learning Design & Leadership, Digital Learning, and Instructional Design. Explore our our programs to see which one fits your needs.