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Generative Artificial Intelligence

 

 Since the release of new generative artificial intelligence (AI) tools, including ChatGPT, we have all been navigating our way through both the landscape of AI in education and its implications for teaching.

Our CTI resources aim to provide support for faculty responding to GenAI tools and their impact on learning. We'll address common concerns and considerations in the context of AI, such as academic integrity, accessibility and ethical uses of the technology. We'll also explore practical applications and pedagogical strategies for teaching and assignment design as you determine what approaches and policies regarding AI are the right fit for your classes.

Cornell’s Core Principles for Generative AI in Education

As we adapt to these quickly evolving tools and observe how students are using them, many of us are still formulating our own values around what this means for our classes. Cornell’s response to generative AI in teaching and learning is built around seven core principles. We invite instructors to consider these principles as they make decisions and talk with their students and colleagues about generative AI and learning:

  • The integrity of the faculty-student relation.
  • A commitment to experimentation, evidence and learning from experience.
  • The centrality of faculty judgment and expertise in the classroom.
  • Responsiveness to real student needs and uses.
  • Recognition of both AI ‘goods’ and ‘harms’.
  • Respect for institutional and disciplinary heterogeneity.
  • The extension and renewal of Cornell’s core mission and values.

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What is Generative Artificial Intelligence (AI)?

Generative artificial intelligence is a subset of AI that utilizes machine learning models to create new, original content, such as images, text, or music, based on patterns and structures learned from existing data. A prominent model type used by generative AI is the large language model (LLM). 

An LLM, like ChatGPT, is a type of generative AI system that can produce natural language texts based on a given input, such as a prompt, a keyword, or a query. LLMs typically consist of millions or billions of parameters that are “trained” on massive amounts of text data, such as books, articles, websites, and social media posts, and can perform various tasks, such as answering questions, summarizing texts, writing essays, creating captions, and generating stories. LLMs can also learn from their own outputs and are likely to improve over time.

It’s important to note that while LLMs can answer questions and provide explanations, they are not human and thus do not have knowledge or understanding of the material they generate. Rather, LLMs generate new content based on patterns in existing content, and build text by predicting most likely words. 

Because of how LLMs work, it is possible for these tools to generate content, explanations, or answers that are untrue. LLMs may state false facts as true because they do not truly understand the fact and fiction of what they produce. These generated fictions presented as fact are known as “hallucinations."

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How Will Generative AI Affect Learning?

Early responses to generative AI in teaching and learning were driven by experimentation. Often that experimentation was informed by an understanding of evidence-based teaching practices, but there was not yet any research to assess the impact of generative AI on learning itself. That research is beginning to appear, with more studies published every month. While some studies have demonstrated learning gains associated with student use of GenAI tools, especially in math or computer science courses,  often those gains have proved ephemeral (Nie et al., 2024 and Bastani et al., 2024) 

Other studies of GenAI’s impact on writing are more concerning, showing that GenAI can lower the ‘friction’ of writing in concerning ways. Students who used GenAI for writing showed less brain activity, their writing showed more homogeneity, and the students themselves were less able to recall their written work when aided by GenAI (Kosmyna et al., 2025). 

At the same time, research is now beginning to focus on particular ways in which GenAI might improve learning, whether by limiting its use to helping students engage in active engagement and practice, providing students with actionable and supportive feedback, and/or helping students reflect on their learning and metacognition. GenAI tools have also begun to introduce ‘study’ modes specifically designed to help students learn using these approaches. 

We continue to monitor the developing research into GenAI and learning, and to let that research inform our support for teaching.  

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Generative AI Literacy

While ChatGPT and other LLMs can assist learners in various tasks and activities, they cannot replace human creativity, judgment, ethics, or responsibility, all of which are essential for learning. Thus, the need for AI literacy is essential for students and faculty alike.

We can think of ethical generative AI literacies as the ability to understand, evaluate, and critically engage with generative AI technologies. Generative AI literacy includes skills such as recognizing when and how generative AI is used in various domains; assessing the reliability and validity of AI-generated outputs; identifying the ethical and social implications of AI applications; and creating and communicating with generative AI systems in ways that are appropriate to your course.

Just as we adapt to the changing media environment, developing AI literacy will be an ongoing process, but one that is vital to helping you and your students become more informed and responsible users and creators of AI technologies.

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Responding to Generative AI in Classes

Students are increasingly using GenAI tools in their daily life, beyond whether they use them for courses. Faculty are increasingly trying to navigate this usage, redesigning courses, assignments, and assessments to best ensure students engage in the cognitive effort that accompanies learning. CTI’s approach is to help faculty both integrate GenAI tools into their courses where faculty perceive them to be beneficial, and to help faculty design courses that help ensure students do not off-load crucial learning to a GenAI tool. 

There remains a wide range of ways in which GenAI can potentially be used by both faculty and students to benefit teaching and learning. These include: 

  • Providing instant access to vast amounts of information quickly.
  • Aiding diverse learners with different learning abilities, linguistic backgrounds or accessibility needs.
  • Accelerating exploration and creativity, spark curiosity, suggest new ideas and ways of thinking.

Students might explore using Generative AI to:

  • Engage in dialogue where the GenAI tool quizzes the student about course content.
  • Explore ideas.
  • Get further explanation of a course topic.
  • Get instant and actionable feedback.
  • Reflect on their learning or engage in metacognitive learning.

Faculty might explore using Generative AI to save time and improve their course materials:

  • Generate content and course materials including lesson plans, quiz questions, sample problems, or writing scenarios
  • Assist in research tasks including analyzing large datasets, identifying patterns, and generating insights and research directions
  • Draft learning objectives, course descriptions, syllabi statements, or course policies

As you and your students prepare to investigate the use of generative AI tools, we recommend discussing course policies and expectations around their use, and clearly communicating with your students when and in what ways use of generative AI tools are permitted – or not. We also recommend that you consider the accessibility of generative AI tools as you explore their potential uses, especially those that students may be required to interact with. Finally, it’s important to take into account the ethical considerations of using such tools. These topics are fundamental if considering using AI tools in your assignment design.

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Stay Engaged and Informed

The range of AI applications and their abilities continue to develop rapidly. Research into the impact of generative AI tool on learning is ongoing, with new results continually being released. Together, these bring both opportunities and challenges for educators wanting to stay current and informed. As the higher education landscape changes with the advent of this new technology, CTI aims to be a dependable partner and resource for faculty working to consider how to respond to generative AI in their courses. Our goal is to support faculty as they navigate these developments and changes, and to ensure that Cornell’s teaching and learning environments remain rigorous and thoughtful spaces where students can learn and thrive. With this in mind, we will continue to provide further resources and opportunities to support faculty and instructors concerned with how GenAI may impact student learning. 

As you further explore, you may be interested in CTI’s generative AI events. If you want to explore generative AI beyond our available resources and events, please reach out to schedule a consultation.

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References

Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, O., & Mariman, R. (2024). Generative ai can harm learning. Available at SSRN, 4895486.

Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X. H., Beresnitzky, A. V., ... & Maes, P. (2025). Your brain on chatgpt: Accumulation of cognitive debt when using an ai assistant for essay writing task. arXiv preprint arXiv:2506.08872.

Nie, A., Chandak, Y., Suzara, M., Malik, A., Woodrow, J., Peng, M., ... & Piech, C. (2024). The GPT surprise: offering large language model chat in a massive coding class reduced engagement but increased adopters exam performances. arXiv preprint arXiv:2407.09975.