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Area of Science:

  • Education Technology
  • Artificial Intelligence in Education
  • Machine Learning Applications

Background:

  • Higher education faces challenges integrating emerging technologies like artificial intelligence (AI).
  • Many professors lack awareness and understanding of AI's potential in the classroom.
  • There is a critical need for information bridge technology to enhance communication and AI adoption.

Purpose of the Study:

  • To predict the future of higher education through the lens of artificial intelligence.
  • To analyze current educational system challenges, including faculty and student issues, and regulatory changes.
  • To explore arguments and challenges surrounding AI implementation in the educational sector.

Main Methods:

  • Developed a use case model using student assessment data.
  • Synthesized data using a Generative Adversarial Network (GAN).
  • Applied machine learning algorithms including Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, CART, Naive Bayes, Support Vector Machines, and Random Forest.

Main Results:

  • Machine learning models were trained and evaluated on synthesized student assessment data.
  • The Random Forest algorithm achieved a maximum accuracy of 58% in predictions.
  • The study highlights the current limitations and potential for AI in educational contexts.

Conclusions:

  • AI presents opportunities to revolutionize higher education, but significant implementation challenges remain.
  • Bridging the gap between human educators and AI systems is crucial for successful integration.
  • Addressing the psychological impact on faculty and students is essential for AI adoption in education.