Transforming Higher Education: 6 Use Cases of AI Tools in Universities

Global Market Insights Inc. in 2021 has published a report projecting that the value of AI in the education market will exceed $20 billion by 2027.

The increasing demand for AI in education is mainly fueled by the advanced capabilities and advantages of AI technology, which are drawing investments and accelerating its adoption across educational organizations.

The integration of AI tools into academic environments offers numerous benefits, from personalized learning experiences to streamlined administrative processes.

Let’s dive into how AI is transforming education, not just as a tool, but as a revolutionary force, with real-world case studies that are positively impacting education globally.

This article explores six key use cases where AI is making a significant impact in universities.

1. Personalized Learning Pathways

AI-driven platforms use ML algorithms to personalize learning experiences for students. By analyzing student performance and learning styles, these tools can recommend tailored content, activities, and study plans. This ensures that students progress at their own pace and focus on areas where they need the most improvement.

Case Study 1: Duolingo, the most downloaded education app in the history of the App Store, has significantly enhanced its personalization capabilities by integrating AI, particularly through its AI system Birdbrain and OpenAI’s GPT-4. 

This innovation has led to notable growth in both user engagement and revenue. Duolingo also utilizes AI-driven chatbots that provide language instruction through automated text conversations.

The successful adoption of AI has resulted in a 39% revenue increase in Q4 2022, a 42% increase in Q1 2023, and a 62% growth in paying subscribers year-over-year.

Case Study 2: Brainly,  an online learning platform, utilized Vision AI to develop a “Snap to Solve” feature for their mobile app, enabling students to get answers by simply taking a photo of their questions. This feature has achieved a 70% students satisfaction rate and contributed to a rise in paid subscriptions.

2. Intelligent Tutoring Systems

Intelligent Tutoring Systems provide one-on-one tutoring experiences that mimic the guidance of a human tutor. These systems analyze students’ interactions and offer personalized feedback and explanations, helping students grasp difficult concepts more effectively.

Case Study 1: Khan Academy utilized GPT-4 from OpenAI to create an AI tutor named Khanmigo. This chatbot is designed to guide students using the Socratic method, encouraging critical thinking and problem-solving rather than simply providing answers. By leveraging natural language processing (NLP), Khanmigo can understand and respond to student questions in real-time, offering interactive simulations and role-playing scenarios to deliver hands-on, real-world learning experiences.

Case Study 2: Knewton is an adaptive learning company that creates platforms to personalize educational content and also offers courseware for higher education. Their technology is utilized by publishers and educational institutions to deliver courses that adjust to each student’s individual needs. 

A major advantage of Knewton AI is its capacity to develop personalized learning paths for each student. By evaluating a student’s performance on assessments and assignments, along with their engagement with the platform’s content, Knewton AI can pinpoint strengths and weaknesses. 

Case Study 3: The University of Murcia introduced an AI chatbot, named Lolf, that successfully answered over 38,708 student inquiries with a 91% accuracy rate.

This tool effectively supported students while greatly improving their motivation, all without necessitating changes to the existing staff structure.

Case Study 4: Harvard University, to address the challenges of scaling individualized instruction and providing round-the-clock support, is integrating an AI-powered chatbot into its flagship computer science course, CS50. Students enrolled in the program will have access to an AI-driven teaching assistant that leverages the advanced language model GPT-4. This virtual instructor offers personalized guidance, code feedback, and comprehensive learning resources available 24/7, enabling students to learn at their own pace and style.

3. Predictive Analytics for Student Success

Universities are increasingly adopting AI-driven predictive analytics to identify students at risk of dropping out or underperforming. Tools like Civitas Learning and Brightspace capture and analyze data on student behavior, attendance, and performance, enabling universities to intervene early and offer targeted support to improve student outcomes.

Case Study 1: Stanford University developed an AI-powered predictive and intervention system to address the urgent need for timely and effective support for students engaged in self-paced online learning.

The AI system monitors student progress on online learning platforms and predicts potential study difficulties. Once a student is identified as at risk, the AI system recommends specific interventions tailored to the individual’s needs. These interventions could range from revisiting previous lessons to providing additional instructional resources or seeking teacher assistance.

Case Study 2: Ivy Tech developed a ML algorithm to identify at-risk students and offer early intervention. To enhance their system, the team migrated to Google Cloud’s integrated AI tool suite and used TensorFlow to build and power their ML model.

Ivy Tech carried out a pilot study using data from 10,000 course sections. The study identified 16,000 students at risk of failing within the first two weeks of the semester, allowing the college to intervene and address non-academic challenges that were affecting the students’ performance.

4. Virtual Classrooms and AI Grading of Written Assignments

AI is also transforming the way courses are delivered through virtual classrooms and AI Grading assistants. Platforms like Zoom and Microsoft Teams, integrated with AI, facilitate interactive online learning experiences. Additionally, AI-powered grading assistants can answer student queries, grade assignments, and even engage in discussions, freeing up faculty to focus on more complex tasks.

Case Study 1: The University of Michigan developed an Automated Text Analysis (ATA) system to address the challenge of providing timely feedback on student writing assignments in large STEM courses.

Researchers from the Michigan AI Laboratory integrated the ATA system into the M-Write program, a broader initiative to improve student writing skills. This innovative approach combines automated analysis with human expertise to enhance student learning.

The AI analyzes essays, provides initial feedback, and identifies areas where students struggle.

Case Study 2: Oak National Academy developed AI-driven tools to automate time-consuming tasks like quiz and lesson plan generation. Leveraging AI algorithms and natural language processing, the academy created an AI quiz generator and an AI lesson planner. These tools produce educational materials tailored to specific subject matter and curriculum requirements. 

5. AI technologies to Transform Audio or Video into Text 

Speech-to-text or transcription services have significantly advanced in recent years. These tools use machine learning and natural language processing (NLP) to automatically convert spoken language into written text, making them valuable in various industries like education, media, customer service, and legal sectors. AI-driven tools like Amberscript and  Berlitz are capable of transforming audio or video into 100% accurate text and subtitling.

Case Study 1: The Amsterdam University of Applied Sciences (HVA) faced challenges in transcribing recorded interviews for research, as manual transcription was time-consuming and impractical. By adopting Amberscript’s AI-driven transcription services, HVA reduced transcription time from three weeks to 5-7 working days, achieving 100% accurate text and subtitles. Amberscript’s speech recognition models, customized for specific languages and supported by the Ministry of Economic Affairs, streamline the transcription process, allowing researchers to focus on more critical tasks. The AI engine initially transcribes with 85%-95% accuracy, which is then manually refined to near-perfect accuracy.

Case Study 2: Berlitz partnered with Microsoft to implement Azure AI Speech technology, an advanced Automatic Speech Recognition (ASR) system. With its advanced models trained on extensive audio data, Azure AI Speech can accurately recognize a wide range of accents, dialects, and speech patterns.

AI has allowed a language education company to significantly improve student experience with pronunciation assessment for 500,000 learners and reduce product development costs.

6. Student Engagement and Retention

AI tools like chatbots and personalized communication platforms are used to enhance student engagement and retention. Chatbots like AdmitHub interact with students in real-time, answering questions about enrollment, deadlines, campus events, and more. This continuous engagement helps keep students informed and connected to the university community.

Case Study 1: Nova Southeastern University implemented the Aible AI tool to address the critical issue of student attrition. Leveraging advanced data analytics and predictive modeling, Aible enabled the identification of students at high risk of dropping out. By employing augmented analytics, augmented data, and ML, Aible uncovered complex patterns within student data. This allowed the university to implement targeted interventions and optimize resource allocation, resulting in a significant 17% reduction in student attrition.

Case Study 2: NVIDIA installed HiPerGator AI, an NVIDIA DGX SuperPOD™, at the University of Florida (UF). This setup includes 140 NVIDIA DGX™ A100 systems, each with eight NVIDIA A100 Tensor Core GPUs and an additional 17,920 CPUs. Completed in just 90 days, it became the largest AI supercomputer ever installed at a university.

Using HiPerGator AI, UF developed GatorTron, the largest clinical language model to date, featuring 3.9 billion parameters. GatorTron is over 10 times larger than the second-largest clinical model and reduced misclassification by 24% compared to BERT-large in the i2b2-2010 named-entity recognition benchmark.

Since its deployment in January 2021, HiPerGator AI has been instrumental in advancing AI education, conducting impactful research, and preparing students for in-demand jobs. To foster AI engagement across various disciplines, UF established the AI Research Catalyst Fund. 

Conclusion

The implementation of AI tools in universities is reshaping the educational landscape, offering enhanced learning experiences, improved administrative efficiency, and greater accessibility. As AI continues to evolve, its integration into higher education will likely deepen, further transforming how universities operate and how students learn. Universities that embrace these technologies will be better positioned to meet the needs of their students and faculty in an increasingly digital world.