George Veletsianos, PhD

Professor, researcher, consultant, speaker, educational technology, learning design, online education, emerging technologies

Category: generative artificial intelligence

AIs dedication to truth, justice or equity

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On January 25, 2023

In generative artificial intelligence, sharing

In response to my post from yesterday, Stephen Downes focuses on the important and difficult issue. He says:

…George Veletsianos focuses on the question, “What new knowledge, capacities, and skills do instructional designers need in their role as editors and users of LLMs?” Using the existing state of chatGPT as a guide, he suggests that “a certain level of specificity and nuance is necessary to guide the model towards particular values and ideals, and users should not assume that their values are aligned with the first response they might receive.” At a certain point, I think we might find ourselves uncomfortable with the idea that an individual designer’s values can outweigh the combined insights of the thousands or millions of voices that feed into an AI. True, today’s AIs are not very good examples of dedication to truth, justice or equity. But that, I’m sure, is a very temporary state of affairs.

Good point: We might find ourselves uncomfortable with that idea. But, here’s the two assumptions that I am making:

1. That individual has developed a dedication to truth, justice, equity, and decolonization that they are able to apply to their work. Yes, I am hopeful on this.

2. For an AI to reflect values aligned with justice, equity, and decolonization, we (aka society) likely need to regulate and re-imagine their design. I am less hopeful on this.

I guess that where Stephen and I disagree is on the future commitments of AI. I would like to be as hopeful as he is, but I am not convinced yet. I would like to be wrong.

AI, ChatGPT, instructional design, and prompt crafting

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On January 23, 2023

In generative artificial intelligence, sharing

David Wiley wrote a thoughtful post on the ways in which AI and Large Language Models (LLMs) can “provide instructional designers with first drafts of some of the work they do.” He says “imagine you’re an instructional designer who’s been paired with a faculty member to create a course in microeconomics. These tools might help you quickly create first drafts of” learning outcomes, discussion prompts, rubrics, and formative assessment items.  The point is that LLMs can quickly generate rough drafts that are mostly accurate drafts, that humans can then “review, augment, and polish,” potentially shifting the work of instructional designers from authors to editors. The post is well worth your time.

The question that I’d like to spend some time thinking about is the following: What new knowledge, capacities, and skills do  instructional designers need in their role as editors and users of LLMs?

To focus on a single item here, much has been written about prompt engineering or crafting, which is the way in which user text is entered to guide LLMs like ChatGPT to generate desired responses. For example, a certain level of specificity and nuance is necessary to guide the model towards particular values and ideals, and users should not assume that their values are aligned with the first response they might receive from a prompt. Below is an example to illustrate how assessment items vary, as the desired assessment changes from good, to efficient, to equitable. My prompt is in bold and ChatGPT output follows.

 

assume that a learning outcome of a course is “understand opportunity cost.” What is a good assessment item for this learning outcome?

A good assessment item for the learning outcome of “understand opportunity cost” would be a case study or scenario-based question that requires the student to analyze and apply their understanding of opportunity cost to make a decision.

For example:

Scenario: A small business owner is trying to decide whether to invest in new equipment or to expand the business by opening a second location. The owner has $100,000 to spend and estimates that the equipment will cost $50,000 and the second location will cost $80,000.

Question: What is the opportunity cost for the business owner in each case and which option would you recommend and why?

This question requires the student to understand the concept of opportunity cost, which is the cost of the next best alternative, in order to make an informed decision about which option would be the best investment for the business. It also allows the student to demonstrate their understanding of the concept by applying it to a real-world scenario.

assume that a learning outcome of a course is “understand opportunity cost.” What is an efficient assessment item for this learning outcome?

An efficient assessment item for the learning outcome of “understand opportunity cost” would be a multiple-choice question that allows the student to quickly and easily demonstrate their understanding of the concept.

For example:

Question: Which of the following is the correct definition of opportunity cost? A) The cost of a good or service B) The cost of the next best alternative C) The total cost of production D) The profit made from a good or service

The correct answer is B) The cost of the next best alternative. This multiple-choice question is efficient as it allows students to quickly demonstrate their understanding of the concept, and it is easy to grade, as there is only one correct answer. This multiple-choice question could be used as a formative assessment to check student’s understanding, and it could be also used as a summative assessment to evaluate their final understanding of the course’s outcome.

assume that a learning outcome of a course is “understand opportunity cost.” What is an equitable assessment item for this learning outcome?

An equitable assessment item for the learning outcome of “understand opportunity cost” would be a problem-solving or open-ended question that allows students to demonstrate their understanding of the concept in a way that is not limited by their background or language abilities.

For example:

Question: Suppose you have $1000 to spend and you want to buy a new computer and a new phone. A computer costs $800 and a phone costs $200. Provide a written explanation of how you would make your choice and what the opportunity cost of your choice is.

This open-ended question allows students to demonstrate their understanding of opportunity cost in their own words and allows them to apply their understanding to a real-world scenario. It also allows for different students to approach the problem differently and does not rely on language or background knowledge. It also allows for the teacher to evaluate student’s understanding of the concept in a more holistic way, and not just based on a single correct answer.

This type of assessment is equitable because it allows all students to demonstrate their understanding of the concept and does not disadvantage students who may struggle with multiple-choice questions or who may have a different background or language abilities.

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