Artificial Intelligence and Andragogical Innovation

Artificial Intelligence and Andragogical Innovation

The presentation includes an in-depth examination of AI’s role in higher education, focusing on enhancing learning, improving outcomes, and navigating the challenges and ethics of AI in education.

Additionally, a video recording of the event is available 

Summary of the Google presentation slides:

1. Prompt Engineering for Text: Different types of AI prompts like descriptive, creative, informative, instructional, and more, used in educational settings.

2. AI and Andragogy Enhancements: How AI improves learning in higher education through personalization, intelligent tutoring, automated grading, virtual reality, chatbots, and more.

3. AI Tools for Higher Education: Listing AI tools and their benefits for students and teachers, including intelligent tutoring systems, adaptive platforms, data analytics, research tools, and language processing.

4. AI in Personalized Learning: AI’s role in analyzing student data for personalized content and curriculum adaptation, using tools like LMS, Adaptive Learning Platforms, and Intelligent Tutoring Systems.

5. Research Enhancement through AI: AI’s assistance in analyzing research papers, data analysis, trend identification, and visualization, using NLP research assistants and data analytics software.

6. Improved Academic Outcomes with AI: AI’s impact on predicting student success, identifying at-risk students, tracking progress, and assessing teaching effectiveness using analytics dashboards and assessment tools.

7. Comparison of AI Tools: Comparing AI tools like Claude, ChatGPT, Elicit, Keenious, and Google Bard, focusing on their uses, features, and benefits.

8. Integrating AI in Higher Education: Steps for implementing AI in education, including needs assessment, tool research, stakeholder engagement, pilot testing, and policy considerations.

9. Ethical Considerations in AI Education: Discussing ethical issues like privacy, bias, transparency, intellectual property, the digital divide, and ethical AI usage.

10. Research Benefits, Challenges, Ethical Considerations: A comprehensive view of AI’s impact on research, covering aspects like accuracy, efficiency, collaboration, bias detection, and resource allocation.

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