Exploring the Role and Use of Generative AI Technology in Teaching
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Introduction to AI in Higher Education
Generative AI is transforming higher education by offering innovative ways to enhance teaching, learning, and administrative processes. Generative AI includes technologies that perform tasks that previously required human intelligence, such as visual perception, speech recognition, decision-making, and language translation. In higher education, AI is being used to personalize learning experiences, create innovative assessment methods, enhance student engagement, provide real-time feedback to students, and more.
Opportunities and Challenges for Instruction
AI presents both opportunities and challenges for instruction in higher education. Opportunities include personalized learning paths, immediate feedback on assignments, virtual tutoring, enhanced accessibility, and efficient grading. Challenges include ensuring equitable access to AI tools, maintaining academic integrity, adapting teaching methods, and keeping pace with rapidly evolving technology.
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Ethical Considerations and Responsible AI Use
Integrating AI into higher education requires considering ethical implications and ensuring responsible use. Key principles include transparency, trust, bias mitigation, data privacy, and accessibility.
Appropriate and Inappropriate Uses of AI
Understanding the appropriate and inappropriate uses of AI in higher education is essential to maximize its benefits while minimizing risks. Appropriate uses include enhancing student engagement, early detection of students at risk, implementing accessibility tools, and personalized learning recommendations. Inappropriate uses include relying solely on AI for high-stakes decisions, replacing human interaction entirely, implementing AI without privacy safeguards, and allowing AI to make final grading decisions without human oversight.
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The Future of AI in Higher Education
As AI continues to evolve, its impact on higher education is expected to grow. A survey by the American Association of Colleges and Universities found that 63% of higher education leaders believe AI will significantly impact teaching and learning in the next 3-5 years; 52% see AI as an opportunity to enhance student learning experiences; and 47% believe AI will improve operational efficiency in higher education institutions.
GenAI Chatbot Scenarios in Higher Education
The GenAI Chatbot Scenarios in Higher Education activity offers the opportunity to explore various potential applications and ethical considerations of generative AI in education and critically assess the pros and cons of different AI scenarios, fostering informed decision-making and thoughtful integration of AI technologies in your teaching practice.
AI Tools Available for Faculty at Stevens
Stevens offers various platforms with built-in AI features and capabilities. For detailed information on these tools and their functionalities, please refer to this knowledge base article.
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Educators typically adopt one of three approaches regarding the use of AI in learning: fully integrating AI, selectively using AI or opting not to use AI at all. This article focuses on the first two approaches, exploring how AI can be leveraged to enhance the teaching and learning experience.
The following resources offer examples of creative and numerous ways educators have integrated generative AI:
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101 Creative Ideas to Use AI in Education: Includes ideas such as using AI to develop variety in scenario-based assessments; utilizing an AI-powered rubric generator; and using AI for critical thinking/evaluation
Teaching with AI Checklist: Offers questions for instructors to consider regarding the effective use of generative AI in their course. For example: "Have I considered why I was using certain take-home assessment types before the widespread use of generative AI? Can I replace them with new approaches that serve the same purpose?”
Writing Case Studies Using Generative AI: Intimate Debate Case Study: Provides guidance on crafting an intimate debate case study, where students analyze evidence supporting both sides of a contentious issue. This approach ensures that the instructor maintains control throughout the design process, utilizing generative AI as a tool for research and creative assistance.
AI Prompts: Offers tips for writing prompts, which are the starting text or questions that are provided to an AI in order for it to generate a response. The effectiveness and relevance of the response largely depend on how well the prompt is crafted.
AI Considerations for Teaching and Learning: Includes considerations related to transparency in instruction. Transparency-related considerations are crucial in teaching and learning with AI. For example, it’s important to set clear expectations for student use of AI in your syllabus.
Using AI Detection Tools with Caution
Stevens has an institutional license for Turnitin, a plagiarism detection tool that compares student submissions against a large database of student work, publications, and materials on the internet. In response to AI, Turnitin has released an AI detection feature. For more information on how the tool works, please refer to AI Writing.
While this feature can help identify potential AI-generated content, it is important to understand its limitations. There are issues with false positives and bias with these tools. No AI detection tool is 100% accurate. Instructors should not use AI detection tools as a definitive way to gauge misconduct. Turnitin states: “[W]e must emphasize that the percentage on the AI writing indicator should not be used as the sole basis for action or a definitive grading measure by instructors.” Research from Stanford highlights biases in AI detectors, which are particularly biased against non-native English writers.
Watch this short video where David Adamson, an AI scientist at Turnitin, explains more about false positives and where we might get it wrong.
Given these limitations, we recommend instructors use a more positive and proactive approach when using AI detection. MIT Sloan’s article, AI Detectors Don’t Work, explores why these tools are unreliable and suggests alternative strategies, such as fostering academic integrity through transparency, engaging assignments, and inclusive teaching practices.
Rethinking Assessment in the Age of AI
Core Principles of Assessment Design
The rise of generative AI challenges traditional assessment methods, but it also presents an opportunity to rethink how we evaluate student learning. The core principles of effective assessment design remain unchanged in the age of AI. Assessment is not just a final step after a course, module, or unit. It should be an ongoing process that occurs before, during, and after learning. While exams provide a snapshot of a student's learning journey, frequent low-stakes assessments offer a more comprehensive reflection of their progress and skill mastery. These principles become even more essential with the increased prevalence of AI.
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Assessing Learning Processes Over Product
When assignments focus on only the final product rather than the learning process, especially high-stakes assessments that account for a significant portion of the overall grade, students are more likely to use generative AI to complete the work without engaging in the learning process. This calls for a thoughtful reflection on how we can assess the learning process and provide ongoing feedback using formative assessment.
In this video (59:27), Christie DeCarolis discussed how Hypothesis, a social annotation tool, assesses learning as an ongoing and visible process, facilitates research collaboration, and supports comprehensive reading. By asking students to annotate readings, respond to peers, and reflect on their understanding, instructors can assess comprehension and critical thinking in a way that AI cannot easily replicate.
In the article Assignment Makeovers in the AI Age, six key questions can help instructors (re)design their assessments:
- Why does this assignment make sense for this course?
- What are the specific learning objectives for this assignment?
- How might students use AI tools while working on this assignment?
- How might AI undercut the goals of this assignment? How could you mitigate this?
- How might AI enhance the assignment? Where would students need help figuring that out?
- Focus on the process. How could you make the assignment more meaningful for students in order to support them more in their work?
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Types of Assessments that Discourage the Use of AI
As generative AI tools continue to improve in accuracy, creativity, and fluency in language generation, designing assignments that completely prevent students from using AI is challenging. However, the following types of assessments and activities can discourage the use of AI while promoting more meaningful and effective engagement with generative AI.
- Authentic assessments that require higher-order thinking skills (e.g., critical thinking, problem-solving, creativity)
Collaborative learning and group work (in person or via breakout rooms in Zoom)
Real-time activities and assessments, e.g., oral exams, video presentations using Panopto, live polls using Poll Everywhere
Prompts that ask students to apply the reading to their own lives or personal contexts
Class discussion participation (in person or via Zoom, e.g., in the Zoom main meeting or in small group discussions in Zoom breakout rooms)
Writing assignments that require close reading or extensive citation and analysis are difficult to replicate with AI, as are writing assignments that ask students to make connections between what they are reading and their own lives.
Bloom’s Taxonomy is a widely used framework for assessing learning levels in higher education, helping instructors align course outcomes, activities, and assessments. Oregon State University has adapted this framework to guide instructors in reflecting on and adjusting course assignments to ensure meaningful learning, while also considering how to thoughtfully integrate AI use. Please refer to this article.
The University of North Dakota's faculty developed the AI Assignment Library, which offers assignments categorized by discipline, course level, and learning outcomes to support student learning in the AI era.
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Guiding Student Use of AI
As we explore AI and plan our course development strategy, it's essential to recognize that students are navigating this journey alongside us. Just like us, students are also curious about how AI will impact their learning and are seeking guidance and direction on how to use AI responsibly and effectively.
In 2024, Oregon State University conducted a survey with 669 students who had taken online courses to assess their knowledge, usage, and perceptions of generative AI tools in their courses and future careers. The study provides important insights into both student understanding and use of generative AI. “Over half of participants indicated that they were at least somewhat interested in receiving guidance from instructors on how to use generative AI tools”.
Developing Course Policies
Establish a clear course policy on whether and how students may use AI tools in your class. If generative AI is permitted in certain areas, clearly communicate your expectations and guidelines in the syllabus.
Provide detailed rubrics and assessment criteria for each assignment. Platforms like Gradescope enable instructors to integrate rubrics into assignments that ensure consistency in grading and transparency for students.
Outline the types of activities where AI is allowed and recommend appropriate tools. Most importantly, explain why these guidelines matter and how they align with the course’s learning outcomes.
Consider the following questions when developing your AI course policy (Adapted from Stanford Teaching Commons Creating Your Course Policy on AI)
- What is the policy and what tools does it apply to specifically?
- When does it apply? What conditions or contexts allow or preclude the use of AI?
- What processes and consequences result from non-compliance?
- What rationale and reasoning guide this policy?
- How do you provide support to students in relation to this policy?
- How does the policy show support for student well-being?
Here are some examples of AI usage statements you may adopt. For more examples, please see classroom policies for AI tools, created by Lance Eaton.
How Students Can Use AI
Start taking some time to engage with some generative AI tools yourself to better understand how students might use them in your course. Have an open and transparent conversation with your students about generative AI. Ask about their prior experiences with these tools, as they may need guidance on using them effectively. Students may need guidance on crafting appropriate prompts, understanding limitations, and evaluating the quality of the information provided. Help prepare them for AI literacy with resources like AI for Students.
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