Artificial Intelligence

The standards Librarians use to assess information literacy competencies and skills are developed by our national professional organizations.

These information literacy standards are directly applicable to learners' mastery of artificial intelligence.

Below we provide example learning outcomes for AI-related information work. These learning outcomes are organized beneath their corresponding standards and performance indicators from The Association of College and Research Libraries’ (ACRL) Health Sciences Interest Group’s (HSIG) Information Literacy Standards for Nursing. Though these standards were created for nurses, they are applicable to all health professionals, and we have replaced the word "nurse" in each standard with "health professional" to reflect the flexibility of the standards.

Standards for information literacy mapped to AI learning outcomes:

1. The information literate [health professional] determines the nature and extent of the information needed.

   1. Performance indicators:
      1. Defines and articulates the need for information.
      2. Identifies a variety of types and formats of potential sources for information.
      3. Has a working knowledge of the literature in [health] related fields and how it is produced.
      4. Considers the costs and benefits of acquiring the needed information.
      5. Reevaluates the nature and extent of the information need.
   2. AI learning outcomes
      1. Identifies general differences between chatbots and search engines in purpose and structure.
      2. Identifies differences between natural-language-processing (NLP) search algorithms like Google Search and non-NLP search engines like PubMed.
      3. Identifies ways to integrate AI into research as a tool.
          

2. The information literate [health professional] accesses needed information effectively and efficiently.

   1. Performance indicators:
      1. Selects the most appropriate investigative methods or information retrieval systems for accessing the needed information.
      2. Constructs and implements efficient and effectively-designed search strategies.
      3. Retrieves information online or in person using a variety of methods.
      4. Refines the search strategy if necessary.
   2. AI learning outcomes
      1. Constructs and implements efficient and effectively-designed search strategies, which account for natural-language processing algorithms on their chosen search engine.

3. The information literate [health professional] critically evaluates the procured information and its sources, and as a result, decides whether or not to modify the initial query and/or seek additional sources and whether to develop a new research process.

   1. Performance indicators:
      1. Summarizes the main ideas to be extracted from the information gathered.
      2. Selects information by articulating and applying criteria for evaluating both the information and its sources.
      3. Synthesizes main ideas to construct new concepts.
      4. Compares new knowledge with prior knowledge to determine the value added, contradictions, or other unique characteristics of the information.
      5. Validates understanding and interpretation of the information through discourse with other individuals, subject-area experts, and/or practitioners.
      6. Determines whether the initial query should be revised.
      7. Evaluates the procured information and the entire process.
   2. AI learning outcomes
      1. Identifies impact of AI use on learning process and learning outcomes.
      2. Validates understanding and interpretation of the information received from a chatbot through discourse with other individuals, subject-area experts, and/or practitioners.
      3. Determines whether the initial query should be revised.
      4. Evaluates the procured information and the entire process.
          

4. The information literate [health professional], individually or as a member of a group, uses information effectively to accomplish a specific purpose.

   1. Performance indicators:
      1. Applies new and prior information to the planning and creation of a particular product.
      2. Revises the development process for the product.
      3. Communicates the product effectively to others.
   2. AI learning outcomes
      1. Communicates their use of AI tools to others.

5. The information literate [health professional] understands many of the economic, legal, and social issues surrounding the use of information and accesses and uses information ethically and legally.

   1. Performance indicators
      1. Understands many of the ethical, legal and socio-economic issues surrounding information and information technology. 
      2. Follows laws, regulations, institutional policies, and etiquette related to the access and use of information resources.
      3. Acknowledges the use of information sources in communicating the product or performance.
   2. AI learning outcomes
      1. Follows laws, regulations, institutional policies, and etiquette related to the access and use of AI tools in the classroom.
      2. Acknowledges the use of AI information sources in communicating the product or performance.
      3. Identifies impact of AI use on their own mental health.
          

There are multiple research-based AI Literacy frameworks:

• ABC model – attitude, behavior, cognition (Knoth 2024)​
• AI Knowledge, attitudes, skills (Mousavi Baigi 2023)​
• Systematic Review of themes to include in AI education (Pupic Nov 2023): theory & application, communication, ethics, quality improvement, collaboration, perceptions & attitudes​
• The Big Ideas in AI - perception, representation & reasoning, learning, natural interaction, societal impact (Touretzky et al 2022) ​

Wang et. al. 2023 created a 4-dimension framework that address themes and some competencies:

• Awareness: be aware of and comprehend AI technology in practical applications; ​
• ​Application: be able to apply and use AI technology for accomplishing tasks proficiently; ​
• ​Critical Thinking: be able to analyze, select, and critically evaluate the data and information provided by AI;​
• ​Social Responsibility: develop awareness of one's own personal responsibilities and respect for reciprocal rights and obligations

AI literacy is sometimes referred to in the research literacture as "algorithmic literacy".

This body of research is the only one we have found thus far (fall 2024) which studies actual human behavior and decision-making while interacting with an aspect of artificial intelligence.

"Algorithmic literacy, involves understanding algorithms and their influence, recognizing their uses, assessing their impacts, and positioning individuals as active agents rather than passive recipients of algorithmic decision-making." (Lo 2024)

Algorithmic Literacy is "the combination of users’ awareness, knowledge, imaginaries, and tactics around algorithms." ​(Swart 2021)

Algorithmic literacy research also illuminates some key patterns and narratives influencing human understanding of AI:

“...users’ sense-making strategies of algorithms are:​ context-specific, ​triggered by expectancy violations and explicit personalization cues." ​(Swart 2021)

In other words, people only notice algorithms when they have an experience online which is disjointed or vastly different from their usual experience.

"However, young people’s intuitive and experience-based insights into news personalization do not automatically enable young people to verbalize these, nor does having knowledge about algorithms necessarily stimulate users to intervene in algorithmic decisions.” (Swart 2021)

This last bit is key. If true, we must reconsider an educational model which posits that greater knowledge increases agency.