AI, Research and Writing - Essential Academic Skills

Timetable

Weekly meetings between March and June 2024.

Module aim (aims)

1. Understanding the fundamentals of AI: Introduce students to the core concepts and principles of artificial intelligence, such as machine learning, natural language processing, and neural networks, to provide a solid foundation for further exploration.

2. Developing research skills: Teach students effective research methods, including how to identify credible sources, evaluate the quality of information, and synthesize findings to support their arguments and conclusions in academic writing.

3. Enhancing critical thinking abilities: Encourage students to think critically about AI applications, ethical implications, and potential biases, helping them to develop a well-rounded understanding of the field and its real-world impact.

4. Building strong academic writing skills: Equip students with essential writing techniques, including how to structure essays, develop strong arguments, and properly cite sources, to ensure their work meets the highest academic standards.

5. Encouraging interdisciplinary approaches: Promote the integration of knowledge from various disciplines, such as computer science, psychology, and ethics, in order to foster a comprehensive understanding of AI and its broader implications.

6. Cultivating collaboration and communication: Foster teamwork and collaboration through group projects and presentations, helping students develop strong interpersonal and communication skills that will benefit them in their academic and professional pursuits.

7. Fostering creativity and innovation: Inspire students to think creatively about the potential applications of AI and to imagine innovative solutions to real-world problems, ultimately preparing them to contribute meaningfully to the field of artificial intelligence.

Pre-requisites in terms of knowledge, skills and social competences (where relevant)

English - B1+ and higher

Syllabus

Week 1: Introduction to the Course

• Course overview and objectives
• Understanding the interdisciplinary nature of AI research and writing
• Introduction to academic skills: research, critical thinking, and writing

Week 2: Fundamentals of AI

• Basic concepts and terminology
• History of artificial intelligence
• Overview of AI subfields: machine learning, natural language processing, robotics, etc.

Week 3: Machine Learning Basics

• Supervised and unsupervised learning
• Key algorithms and techniques
• Applications of machine learning

Week 4: Research Methods and Information Literacy

• Identifying credible sources
• Evaluating the quality of information
• Effective note-taking and organization

Week 5: Critical Thinking and AI Ethics

• Introduction to critical thinking
• Ethical considerations in AI research and applications
• Bias and fairness in AI systems

Week 6: Academic Writing Foundations

• Essay structure and organization
• Developing strong arguments
• Proper citation and referencing

Week 7: Natural Language Processing

• Fundamentals of NLP
• Applications and examples
• Challenges and limitations

Week 8: Interdisciplinary Approaches to AI Research

• Integration of knowledge from computer science, psychology, and ethics
• Case studies of interdisciplinary AI research projects

Week 9: Group Project Preparation

• Forming project teams
• Identifying research topics
• Proposal development and submission

Week 10: Robotics and AI

• Introduction to robotics
• AI in robotics: autonomous systems, navigation, and control
• Real-world applications and challenges

Week 11: Collaboration and Communication

• Effective teamwork strategies
• Presentation skills and techniques
• Peer feedback and constructive criticism

Week 12: Creativity and Innovation in AI

• Brainstorming and problem-solving techniques
• Innovative AI applications and case studies
• Encouraging creativity in AI research and development

Week 13: Group Project Work

• Research, writing, and collaboration on group projects
• Scheduled consultations with the instructor for guidance and feedback

Week 14: Group Project Presentations

• Oral presentations of group projects
• Q&A and peer feedback sessions

Week 15: Course Wrap-up and Reflection

• Review of key concepts and skills
• Personal reflections on learning and growth throughout the course
• Evaluating future opportunities in AI research and writing

Reading list

1. Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

   • A comprehensive textbook covering the fundamentals of AI and its subfields.

2. Bishop, C. M. (2021). Pattern Recognition and Machine Learning. Springer.

   • An essential resource on machine learning techniques and applications.

3. Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Draft available at https://web.stanford.edu/~jurafsky/slp3/

   • A widely-used textbook on natural language processing and speech recognition.

4. Booth, W. C., Colomb, G. G., & Williams, J. M. (2016). The Craft of Research (4th ed.). University of Chicago Press.

   • A guide to effective research methods and strategies for academic writing.

5. Graff, G., & Birkenstein, C. (2018). "They Say / I Say": The Moves That Matter in Academic Writing (4th ed.). W. W. Norton & Company.

   • A practical guide to improving academic writing skills and structuring strong arguments.

6. Wallach, W., & Allen, C. (2009). Moral Machines: Teaching Robots Right from Wrong. Oxford University Press.

   • A thought-provoking exploration of ethics and AI, including discussions on bias and fairness.

7. Bostrom, N., & Yudkowsky, E. (2014). The Ethics of Artificial Intelligence. In K. Frankish & W. M. Ramsey (Eds.), The Cambridge Handbook of Artificial Intelligence (pp. 316-334). Cambridge University Press.

   • An article covering ethical considerations and challenges in AI research and applications.

8. Browne, M. N., & Keeley, S. M. (2017). Asking the Right Questions: A Guide to Critical Thinking (12th ed.). Pearson.

   • A book that teaches critical thinking skills through questioning and analysis.

9. MIT OpenCourseWare. (n.d.). Artificial Intelligence. Retrieved from https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/

   • A collection of course materials, including lectures, readings, and assignments, from MIT's AI course.

10. TensorFlow. (n.d.). Learn. Retrieved from https://www.tensorflow.org/learn

    • An online resource offering tutorials and examples for building machine learning models using TensorFlow.

11. OpenAI. (n.d.). Research. Retrieved from https://openai.com/research/

    • A collection of research papers, blog posts, and demos related to cutting-edge AI research and development.

Other articles will come from the following academic journals:

• Artificial Intelligence
• Journal of Machine Learning Research
• Journal of Artificial Intelligence Research
• Natural Language Engineering
• IEEE Transactions on Robotics