Pedagogical Support

Leveraging AI for Automatic Grading & Feedback

Instructors have already been able to automatically grade student assignments and provide immediate feedback, allowing students to receive timely guidance on their work. One example would be creating quizzes in Canvas–it is possible to not only pre-program the correct answers for auto-grading, but preload feedback students would receive automatically based on the answer they selected. This strategy is helpful for students for both formative and summative assessments. But AI can do more than automated grading. NJIT provides access to AI-based instructional tools like Gradescope and Harmonize. In these tools, AI is leveraged to interpret handwritten assignments and classify student responses (Gradescope), and help instructors generate prompts based on course learning outcomes, and suggest styles for responding to student work (Harmonize). Instructors can learn more about using these tools by visiting NJIT's Instructional Technology page. 

Using AI to Develop Better Assessments

AI can be used to help instructors develop better assessments by "workshopping" assignment prompts/instructions. Instructors can use AI to generate different versions of an assignment prompt and receive feedback on each version. This allows instructors to refine their prompts to ensure clarity and relevance to the learning objectives. For example, instructors can draft their assignment prompt as they usually do, but before sharing with students, submit the prompt to a generative AI tool such as ChatGPT and ask the AI to read it from a student perspective and identify potential questions, points of confusion, and ask for suggestions to improve clarity of instructions. By leveraging AI in this way, instructors can create assessments that are more effective in gauging student understanding and promoting meaningful learning experiences.

Current Research in AI in Education

An annotated bibliography of current research articles on AI in education provides insights into the latest developments and best practices. For example, recent studies have explored the effectiveness of AI in enhancing student engagement, improving learning outcomes, and personalizing learning experiences. By summarizing key findings and methodologies, this annotated bibliography helps instructors stay informed about the latest trends and research in AI in education.

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510

Annotation: This review article provides a detailed examination of AI applications in education, focusing on the advancements and practical uses of generative AI. It discusses intelligent tutoring systems, automated grading, and personalized learning environments. The authors offer insights into how generative AI can transform traditional educational practices, making it essential reading for faculty interested in cutting-edge educational technology.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AIED-Book-Excerpt-CCR.pdf

Annotation: This book explores the potential benefits and challenges of integrating AI into educational systems, with a specific focus on generative AI. It covers various AI applications, including personalized learning and intelligent tutoring systems. The authors provide practical examples and case studies, making it a practical guide for educators aiming to implement AI in their teaching practices.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-019-0171-0

Annotation: This comprehensive review analyzes the current state of AI applications in higher education, emphasizing the role of educators in integrating AI technologies. The authors identify trends, challenges, and opportunities, providing a rich context for understanding how generative AI can enhance teaching and learning. This source is particularly valuable for faculty seeking a broad overview of AI's impact in educational settings.

Assignment Ideas for Using AI with Students (by Discipline)

This page includes discipline-specific assignment ideas can help instructors brainstorm ways to incorporate AI into student learning experiences. These assignment ideas not only enhance student engagement but also help students develop valuable skills in AI application within their field of study.

Examples:

• Computer Science: Students can use AI to develop algorithms for solving complex problems, such as image recognition or natural language processing. They can also create AI-powered games or simulations to explore different concepts in computer science.

• Engineering: Students can use AI to design and optimize structures or systems. For example, they can use AI to analyze data from sensors and make real-time adjustments to improve the performance of a system.

• Mathematics: Students can use AI to explore advanced mathematical concepts or solve complex equations. They can also use AI to analyze data sets and identify patterns or trends.

• Biology: Students can use AI to analyze genetic data or model biological systems. For example, they can use AI to predict the effects of different genetic mutations or simulate the behavior of a biological system under various conditions.

• Physics: Students can use AI to analyze experimental data or simulate physical phenomena. They can also use AI to model complex systems, such as the behavior of particles in a collider or the dynamics of a galaxy.

• Business: Students can use AI to analyze market trends and consumer behavior. For example, they can use AI to develop predictive models for forecasting sales or identifying potential business opportunities. They can also use AI to optimize business processes, such as supply chain management or customer relationship management.

• Architecture: Students can use AI to design and visualize architectural projects. For example, they can use AI to generate 3D models of buildings based on design requirements and constraints. They can also use AI to analyze building performance, such as energy efficiency or structural integrity, and optimize designs accordingly.

Rethinking Evaluation Criteria for AI-Assisted Assignments

Instructors may need to re-evaluate their evaluation criteria for assignments when students use AI to complete tasks. For example, when assessing an AI-generated project, instructors may need to consider the student's understanding of the AI algorithms used, the creativity and critical thinking demonstrated in applying AI to the task, and the overall effectiveness of the AI solution. New criteria may need to be considered for evaluation, such as prompts used, and strategies students used to refine and revise prompts to re-work the AI output to the final product. Additionally, students may be asked to submit reflection pieces as part of their grade, justifying their process and demonstrating how they evaluated the AI output to determine when they arrived at a satisfactory result. By rethinking evaluation criteria, instructors can ensure that assessments remain relevant and fair in an AI-enhanced learning environment, and that students develop essential skills for critically assessing AI-generated outputs.

Adaptive Learning

Adaptive learning refers to the use of AI to personalize learning experiences for students based on their individual needs and preferences. At NJIT, Canvas, the Learning Management System, can be leveraged for adaptive learning. For example, instructors can use Canvas to track student progress and performance data, allowing them to identify areas where students are struggling and provide targeted support. Additionally, Canvas can be used to deliver personalized content and assessments, enabling students to learn at their own pace and in a way that is tailored to their learning style. By leveraging Canvas for adaptive learning, instructors can create more engaging and effective learning experiences for students.

Assumptions around Student Access and Prior Knowledge

Instructors should be mindful of assumptions regarding students' access to technology and prior knowledge of AI. For example, not all students may have access to high-speed internet or the latest AI tools (e.g., ChatGPT 4 vs the free version), which could affect their ability to fully participate in AI-enhanced learning activities. Similarly, some students may have limited prior knowledge of AI concepts, requiring instructors to provide additional support and resources to ensure equitable learning experiences. This is especially important for courses in which students are encouraged or required to use AI in some capacity. By addressing these assumptions, instructors can create inclusive learning environments where all students can succeed.