Algorithmic Grading Models
These grading tools leverage artificial intelligence to expedite and enhance the grading process. These tools utilize machine learning to automate the grading process and also provide analytical and feedback resources for students and faculty. Many of these tools integrate directly into popular Learning Management Systems reducing the need for multiple submissions.
- Gradescope: An innovative assessment platform designed to streamline the grading process for educators. By leveraging AI-assisted grading, Gradescope facilitates quicker and efficient evaluation of both handwritten and typed assignments. This tool ensures fair and uniform grading across different sections of a course. The platform supports collaborative grading, enabling multiple graders to work together seamlessly. Additionally, Gradescope offers powerful analytics that provide detailed insights into student performance, helping educators identify areas where students may be struggling. This tool markets implementation capabilities with various learning management systems to lower barriers to use.
Leaving an indelible mark on the landscape of tomorrow.
What is Gradescope
Use Case
Bentley University has implemented Gradescope into their native LMS. Targeting STEM courses specifically, Gradescope has provided Bentley’s faculty with the ability to quickly mark both online and paper assignments by applying rubrics equitably. Bentley identifies the ability to mark a flexible range of assignments, apply and change rubrics fluidly, create grading workflows, and extensive students feedback and analytic options as the most valuable facets of this tool.
Bentley expresses the goal to direct faculty time towards supporting students and focusing on teaching and learning. Additionally, Bentley believes that the introduction of this tool bolsters fairness and equity in the grades allotted to students. Bentley provides a good example of institutions altering the expected tasks and responsibilities of faculty without decreasing their involvement with their students. The goal in this use case was not to replace faculty with AI, but rather to alleviate the time constraints cumbersome marking places on faculty.
Chatbot Based Assessment Tools
AI chatbots can revolutionize the way student assessments are conducted by enhancing efficiency, flexibility, and adaptability. These chatbots can be tailored for specific courses, acting as dedicated specialists that provide targeted support to students. They can be programmed to grade assignments and offer personalized feedback, ensuring each student receives the guidance they need. Additionally, chatbots can generate unique assignments that cater to individual learning requirements, fostering a more personalized and effective learning experience. By integrating AI chatbots into educational assessments, institutions can create a more dynamic and responsive learning environment.
Furthermore, AI chatbots can provide instant feedback, helping students to quickly understand and correct gaps in knowledge. This immediacy can enhance learning retention and encourage continuous improvement. Chatbots can also track student progress over time, identifying patterns and offering insights that educators can use to tailor their teaching strategies. By reducing the administrative burden on educators, chatbots free up more time for them to focus on direct student interaction and high-quality instruction. Overall, the adoption of AI chatbots in assessments can lead to a more engaging, efficient, and data-driven educational experience.
Use Case
At the University of Delaware, Jevonia Harris and her team have developed an innovative solution called UD Study Aid, which leverages AI to enhance student learning and engagement. The system utilizes the university’s extensive archive of lecture recordings, running them through AWS Bedrock’s foundation model to extract key topics, subtopics, and definitions. Faculty members then review and approve these AI-generated study materials, ensuring accuracy and relevance
This approach not only highlights the expertise of the faculty but also provides students with tailored study aids, such as flashcards and summaries, directly linked to their course content. The chatbot interface allows students to interact with these resources seamlessly, fostering a personalized learning experience. By maintaining a secure, walled garden for data and emphasizing human oversight, UD Study Aid ensures both the integrity of the educational content and the privacy of faculty data, ultimately supporting a more effective and efficient learning environment.
Computer Adaptive Testing
Computer Adaptive Testing (CAT) provides a unique opportunity to create assessments that cater to individual student needs. This type of testing uses Artificial Intelligence and advanced algorithms to adjust tests in real-time. These tools help ensure that tests are paced in a way that reduces test anxiety and enables students to demonstrate their knowledge more effectively. CAT creates a more equitable testing experience for all students by minimizing external factors that can impact performance. This type of testing can be used in both summative and formative assessments, providing effective indicators to guide students’ studying and revision activities. Additionally, CAT offers more in-depth feedback on challenging areas, ensuring students can better identify and seek the support they need.
Use Case
The Regulatory Exam (REx-PN) offered by the College of Nurses of Ontario is a critical entry-to-practice examination designed for individuals seeking to become Registered Practical Nurses (RPNs) in Ontario. The exam assesses the essential knowledge, skills, and judgment required to ensure safe and competent nursing practice at the onset of their careers.
Traditional fixed-form testing methods may not adequately measure a candidate’s ability level due to their inability to adapt to individual performance variations. This can result in less accurate assessments of a candidate’s true competency.
Implementing Computerized Adaptive Testing (CAT) for the REx-PN offers a dynamic and individualized approach to assessing candidates’ abilities. CAT adjusts the difficulty of questions in real-time based on the candidate’s performance, providing a more precise measure of their competency.