Baker Tilly's Insights on the Intentional AI and Data Analytics in Higher Education - Seite 2

Additionally, AI is being utilized by faculty, students and staff across institutions to streamline administrative processes, enhance teaching and learning experiences and access educational resources more efficiently - freeing up time and resources for other value-added opportunities. These AI uses include:

• Personalized learning: Tailoring educational experiences to individual student needs and learning styles, including adoptive learning platforms that adjust content and pace based on student performance
• Automated grading and assessment: Assisting in grading assignments and exams, particularly for objective questions, saves time for educators and allows for more consistent and unbiased evaluation
• Virtual tutors and assistants: Chatbots or virtual assistants can provide students with additional support and resources, answering questions and offering guidance on a wide range of topics
• Predictive analytics: By analyzing student data, AI can predict student performance and identify those at risk of failing or dropping out, allowing for early intervention and support
• Enhanced research: Processing and analyzing large data sets more efficiently than humans can aid in complex research projects across various fields
• Curriculum development: Helping design curricula by analyzing job market trends and predicting future skills requirements ensures that educational programs remain relevant and effective
• Language learning: AI-driven language learning tools can provide personalized feedback and immersive experiences, helping students to learn new languages more effectively
• Content creation and curation: Assisting in creating and organizing educational content ensures it is up-to-date, relevant and tailored to student needs
• Fraud detection and academic integrity: Helping detect plagiarism and other forms of academic dishonesty ensures the integrity of academic work
• Administrative automation: Streamlining administrative tasks like scheduling, enrollment and student inquiries makes the process more efficient for both students and staff
• Admission management: AI applications help to auto-generate communication and workflow prompts and predict highest potential students (i.e., enrollment probability and positive student outcomes) for recruitment focus

Overall, the integration of AI and data analytics is fostering a more adaptive, efficient and student-centered higher education ecosystem, poised to meet the evolving needs of students and staff in the digital age.