Active learning has been proven to be more effective than traditional teaching approaches, yet implementing this approach can be “labor-intensive” and “challenging to scale” (Kosslyn, 2023, p. 11). According to Kosslyn (2023), incorporating AI into active learning can create a new learning modality that blurs the line between asynchronous and synchronous courses, by empowering every stage of active learning, from designing, and delivering, to assessing:
In this article, we will focus on how AI can help scale active learning in three key stages: Designing, Delivering, and Assessing
AI can help automate many manual tasks during the creation stage of active learning, from drafting learning objectives and activities that can address these goals, to personalizing the exercises to students’ preferences and backgrounds, and more. Below you will find detailed examples of using AI in active learning design:
Social annotation – the method in which study materials are enriched with guided discussion and question prompts has been proven to be an effective way to encourage active engagement with the content. By responding to the annotated questions, learners are prompted to carefully study the materials, thus gaining a deeper understanding of the concepts. However, it can take lots of time to come up with quality, meaningful discussions or questions that generate meaningful answers from students. This is where AI can support.
Acai Engagement Assistant is an AI feature that can automatically generate open-ended questions and corresponding answers based on the content of study materials. This helps save educators valuable time and ensure that the prompts generated are contextually appropriate and pedagogically sound.
This is how teachers can use the Engagement Assistant for prompt creation:
AI can help deliver active learning exercises effectively and efficiently, by streamlining the feedback and reflection process, increasing student engagement and critical thinking, and more. Teachers can either use AI to help facilitate different stages of the activity or have learners engage in interactions with AI (Kosslyn, 2023).
Providing constructive, personalized feedback on students’ writing usually takes up a lot of time for teachers. In a large student cohort, there is a need to scale while personalizing the feedback to encourage students’ learning.
Dr. Adam Cardilini, lecturer at Deakin University used FeedbackFruits Acai’s Writing Coach feature to provide detailed, real-time, and actionable feedback on each writing assignment of 250 students of diverse international backgrounds. Throughout the course, students could opt to use the feature inside their D2L environment to receive instant suggestions on lower-order writing skills such as grammar, citation, and style. Based on this feedback, they could iterate on an improved final submission, as well as increase their autonomy and self-guidance throughout the learning experience.
Dr. Cardilini remarked on the benefits of using AI:
"Ultimately I'd like to provide detailed feedback for every single assignment but that's unrealistic. Automated Feedback did something I couldn't provide for students."
An important part of the feedback process is letting students know how to deliver quality and constructive feedback. However, students’ feedback often falls into the either too short, too positive, or too negative spectrum. This is due to a lack of guidance and also an unwillingness to complete the activity. It is also challenging and time-consuming for instructors to follow, and provide instruction for each student in their feedback delivery process. AI can be leveraged to offer timely instructions for students to give feedback and save teachers a significant amount of time.
The University of Akron uses the Feedback Coach feature of FeedbackFruits Acai, which guides students in providing high-quality, constructive feedback to their peers. This feature helps ensure that peer evaluations are specific, actionable, and aligned with the assignment criteria. By monitoring student engagement and performance, the feature provides insights that can identify and alert instructors to potential issues early on.
Wendy Lampner, Director of Online, Continuing, and Professional Education at the University of Akron, highlighted the benefit of using AI, noting:
"I used to spend a lot of time trying to find out if my students were doing okay, if anyone had disappeared, especially when you teach online that can happen. Now, with AI tools, we can get alerts when a student hasn't engaged for a period of time. This helps ensure that no one falls through the cracks."
That's why we developed the Automated Feedback Coach to assist higher education educators in guiding students to deliver better feedback.
Nathan Riedel, Instructional Technologist at Fort Hays University believes that nurturing a growth mindset and lifelong skills, especially in the age of AI has become the core mission of higher education.
That’s why Nathan came up with several learning activities to help students develop not only critical reflection of AI-generated content but also feedback and collaboration skills.
For this activity, students were required to think of several questions and use ChatGPT to generate the answers and then upload them to FeedbackFruits Assignment Review. Within this tool, the instructor closely analyzed the AI-generated content, annotated important sections, and provided comments, feedback, and questions, even gave suggestions on how to curate the prompts for AI. This process can help students to identify the “deficiencies in AI-generation information, as well as encourage them to go out and look for these holes, or what is lacking in this language model”, as stated by Nathan
This same critical analysis activity can be upgraded into a peer and group assessment component, in which students upload their ChatGPT-generated answers and give feedback on each other’s submissions.
This activity update would normally take up plenty of time for assigning peer reviewers or group members, developing feedback rubric criteria, and such. However, Nathan used FeedbackFruits Peer Review to streamline the entire review process.
Within the tool, students easily upload their AI content and get assigned to another peer’s work to review based on a set of criteria. They can also annotate the submission and add questions or discussion points for further exchange.
Check out these templates for integrating AI in active learning
Aside from designing and facilitating active learning activities, AI can also enhance the assessment phase, speeding up the creation of rubrics, tracking students' progress and automating the grading process.
Rubrics help instructors assess performance, communicate expectations, add a layer of structure to observations, and can play a role in students' formative learning process (Andrade & Du, 2005).
For a long time, rubrics have been primarily used in summative assessment in a teacher-centered way, meaning they are mostly used to quickly and reliably grade student work. Now there seems to be an increasing shift towards using rubrics in a student-centered way, which focuses on student learning (Ragupathi & Lee, 2020).
When used in formative assessment rubrics help students ask and reflect on questions such as: Where am I going? Where am I now? And where am I heading next? (Hattie & Timperley, 2007). Facilitating a learning environment where students can ask themselves these questions is crucial in boosting engagement and active learning.
According to Kosslyn (2023), AI (particularly GPT 4.0) is “remarkably good at creating and using rubrics” (p.265). By giving the right prompts, teachers can save plenty of time creating holistic rubrics for assessment and evaluation.
An effective prompt for rubric creation needs to include
Below is a prompt example, inspired by AI for Education
You are [job role], skilled in creating assessments and evaluating student work. Your task is to create a detailed rubrics for my [student level and major] class studying [topic]. The students are doing [assignment name], in which they [assignment description]. The learning objective of the assignment [learning objectives].
Create the rubric as a chart and include the following criteria [criteria names] and performance levels [level names].
The grading process, which requires careful consideration of students’ deliverables against the evaluation criteria and coming up with a mark that truthfully reflects students’ performance is challenging and time-consuming, especially in large student cohorts. Not only can AI help create rubrics for assessment, but it can also support the grading process by generating grades based on the given criteria.
For example, FeedbackFruits Acai Grading Assistant helps instructors provide faster, more consistent, and personalized feedback at scale. The Grading Assistant analyzes students’ submissions and then provides grade suggestions and feedback based on the rubric. Teachers only need to review these grading suggestions, adjust, and decide on the final grade, which gives them more time to focus on teaching and student engagement.
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Kosslyn (2023) emphasizes that though AI can be helpful in grading, teachers need to check that AI follows the rubric appropriately and gives the correct mark to students.
Andrade, H., & Du, Y. (2005). Student perspectives on rubric-referenced assessment. Practical Assessment, Research, and Evaluation, 10(1), 3.
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81-112.
Kosslyn, S. M. (2023). Active learning with AI: A practical guide.
Ragupathi, K., & Lee, A. (2020). Beyond fairness and consistency in grading: The Role of Rubrics in Higher Education. In Diversity and inclusion in global higher education (pp. 73-95). Palgrave Macmillan, Singapore.
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