Artificial Intelligence for Research and Scholarship

While many generative AI tools are freely available and by definition intuitive to use, getting the most out of these resources for research purpose is benefits from a strategic approach to prompting and refining inquiries. This page highlights some of the contemporary concepts and frameworks being applied to using AI for instruction and research.

 

AI Pedagogy Project:

This interactive guide includes background information, considerations, and tutorials on navigating generative AI environments through an educational lens.  Geared toward instructors, the AI Pedagogy Project also offers guidance on how to incorporate these tools into classroom assignments and activities.

 

AI Information Literacy Module (University of Maryland) :

This interactive module focuses on key areas of AI literacy for students and scholars with particular reference to:

• The basics of how AI systems work (including risks and benefits)
• Strategies for recognizing when AI gives inaccurate or misleading answers
• Tools for fact checking AI and citing AI-generated output
• Exploring  creative ways to use these tools

 

Promoting Students' AI Literacy (Oregon State University):

Introduces AI literacy as a concept and highlights various studies and frameworks for students teaching and learning.

The CLEAR framework (Lo) provides a structured approach to prompt engineering with its five key elements - Concise, Logical, Explicit, Adaptive, and Reflective prompts. This aims to optimize the effectiveness of AI models like GPT in generating high-quality responses. 

Element	Definition
Concise	Brevity and clarity in prompts to guide the AI model to focus on core elements of the task.
Logical	Structured and coherent prompts to maintain a logical progression of ideas and help the AI model grasp context and connections.
Explicit	Clear specifications of the expected output format, content, or scope to minimize irrelevant responses.
Adaptive	Flexibility and customization in prompts through experimenting with different phrasings, structures, and temperature settings.
Reflective	Ongoing assessment and refinement of prompts and techniques through evaluation of AI-generated responses to improve future prompts.

 

 

Being AI Literate does not mean you need to understand the advanced mechanics of AI. It means that you are actively learning about the technologies involved and that you critically approach any texts you read that concern AI, especially news articles. 

We have created a tool you can use when reading about AI applications to help consider the legitimacy of the technology.

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Reliability

Objective

Bias

Ownership

Type

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Reliability

• How reliable is the information available about the AI technology?
• If it’s not produced by the party responsible for the AI, what are the author’s credentials? Bias?
• If it is produced by the party responsible for the AI, how much information are they making available? 
  • Is information only partially available due to trade secrets?
  • How biased is they information that they produce?

 

Objective

• What is the goal or objective of the use of AI?
• What is the goal of sharing information about it?
  • To inform?
  • To convince?
  • To find financial support?

Bias

• What could create bias in the AI technology?
• Are there ethical issues associated with this?
• Are bias or ethical issues acknowledged?
  • By the source of information?
  • By the party responsible for the AI?
  • By its users?

Owner

• Who is the owner or developer of the AI technology?
• Who is responsible for it?
  • Is it a private company?
  • The government?
  • A think tank or research group?
• Who has access to it?
• Who can use it?

Type

• Which subtype of AI is it?
• Is the technology theoretical or applied?
• What kind of information system does it rely on?
• Does it rely on human intervention? 

 

Hervieux, S. & Wheatley, A. (2020). The ROBOT test [Evaluation tool]. The LibrAIry. https://thelibrairy.wordpress.com/2020/03/11/the-robot-test