https://mathpix.com

https://simpletex.net

https://docs.google.com/presentation/d/1EiGxC8xxFmSUsdzoC6YALCuj2qm_5BACqVpTjBq6XQo/edit?usp=sharing

• I don't think it is ethical for me to use Gen AI in my course development because I am "cheating" by asking a computer to do my work.

• Using Gen AI to create scripts, assignments, learning objectives, etc. is not my writing so it sounds different.

• I don't have time to learn the new tools.

• etc.

Integrating AI into the curriculum involves using it as a tool to enhance learning rather than viewing it as a threat. The discussion can center on using demonstrations and employing AI-based tools for simulations or data analysis. It is also important for both students and faculty to become aware of AI capabilities, limitations, ethical concerns, and potential biases.

Assignments may have to evolve to focus more on measuring critical thinking creativity, decision making where students justify why they followed a certain thought process. By designing complex projects that integrate presenting their thought process and decisions they took may be one way to mitigate AI doing the work for the students.

Does the use of AI create less of a connection between faculty and students? Is there a loss of the human touch? How can AI leverage the faculty members time and efficiency to better enhance the course and allow faculty time to actively engage with their students.

How can we ensure that AI enhances rather than undermines the quality of education? As AI technology continues to advance, it brings with it several challenges and limitations that we must navigate such as:

• Technocentrism:

• AI/Innovation Fatigue: Burnout resulting from the continuous need to adapt to new AI tools and technologies.

• Sustainability: Using an avatar or AI assistant to attend meetings or allow you to multitask sounds great- but what happens when everybody does it? Are we considering AI tools through this filter?

• Gullibility: Acceptance of AI outputs without critical evaluation.

• Misperceptions of How LLMs Work: Misunderstandings about large language models, such as thinking they have a database, memory, as well as anthropomorphism.

• Misuse of ChatGPT: For instance, using chatgpt as an AI detector, production of low quality content.

• Education Value Questions: What happens when AI is used simultaneously on both ends of the academic experience - by students for generating assignments, and instructors to grade and give feedback on assignments?

  
  

• Motivation: What problems are we trying to address with the use of generative AI?

• Outcomes: What does/would the use of a tool look like in practice? What was the process and the final result?

• Effectiveness: Are we ready to take the next step and adopt certain tools? What criteria should be used to make the final decision?

We will discuss real-world examples of AI implementation in instructional design. Discuss both the successes and the challenges faced. Highlight practical tips for managing expectations and maximizing efficiency.

Overview:

The accessibility and affordability of Artificial Intelligence (AI) learning platforms are transforming the educational landscape. Proponents argue that AI can deliver personalized, on-demand learning for a fraction of the cost of traditional universities. This raises the question: is Higher Education becoming irrelevant in the face of AI-powered learning?

Arguments for AI Replacing Higher Education:

• Cost: AI platforms offer significantly cheaper access to a vast amount of knowledge.

• Accessibility: Learning becomes flexible and available anytime, anywhere.

• Personalization: AI tailors learning to individual needs and pace, potentially leading to faster and more efficient knowledge acquisition.

Arguments for the Continued Importance of Higher Education:

• Human Interaction: Universities provide valuable in-person interaction with professors and peers, fostering critical thinking, communication, and teamwork skills – crucial for success in the workforce.

• Credentials: Degrees from accredited institutions hold weight in the job market and are often required for certain professions.

• Structured Learning: Universities offer a structured learning environment that provides guidance and deadlines, beneficial for some students who thrive with such a framework.

• Specialized Fields: For specialized fields, universities offer in-depth research opportunities and access to cutting-edge facilities not readily available through AI platforms.

The Future of Education:

The debate is not necessarily a binary choice. The future of education might involve a blended model that leverages the strengths of both AI and traditional methods:

• AI-powered learning can personalize foundational knowledge acquisition.

• Universities can focus on developing critical thinking, communication, and social skills that complement AI learning.

• HigherEd institutions may specialize in offering accredited degrees and fostering groundbreaking research alongside AI platforms.

Debate Prompt: Consider the arguments for both sides and weigh in: Will AI make Higher Education obsolete, or will the two find a way to co-exist and create a more powerful learning experience?

•Stand alone tools will be adopted by faculty to bypass development processes.

•If the development is collapsed or squeezed out of the development cycle, then is the role of instructional designers going to be focused on humanizing education

What does humanizing mean in this context?