Core Competencies

The following list outlines the core competencies that can be used to gauge learners' understanding of key AI literacy concepts.

1. Basic AI Literacy - establish a baseline understanding of AI concepts, laying groundwork for future discussion of generative AI.
   • Individuals will be able to recognize different types of AI. This will enable them to evaluate the potential benefits and risks, understanding how AI can impact them or their business processes.
2. Knowledge of GenAI Tools - provides a peripheral understanding of the workings of generative AI.
   • Despite the extensive media coverage, ChatGPT lacks widespread recognition among diverse demographics, particularly among certain age groups and professions. Even among those who have heard of it, usage remains limited.
3. Knowledge of the Capacity and Limitations of GenAI Tools - equips individuals with the proficiency to assess the capabilities and constraints of genAI tools.
   • The output of ChatGPT and other LLMs can often contain a mixture of facts and completely false and fabricated statements, and awareness of this is fundamental.
4. Skill to Use GenAI Tools - promotes practical proficiency to effectively leverage generative AI tools in diverse contexts.
   • A user who needs to generate images should start to learn and practice several tools for this, such as Midjourney, Adobe Firely (available in Photoshop), DreamStudio, and others.
5. Ability to Detect AI-generated Content - teaches the skill of discerning AI-generated content.
   • When seeing a video that looks questionable, people should be able to verify, through AI detectors and official sources, whether the video is authentic or fabricated. A recent example from 2023 is the case of a viral AI-generated video of the Pentagon on fire, which created some panic.
6. Ability to Assess the Output of Generative AI Tools - provides the ability to critically assess output quality, relevance, and potential biases.
   • Individuals using LLMs to write essays on specific topics should check the facts presented in the output, to verify that they are not hallucinations of the model, and present these facts in a way that corresponds to their needs.
7. Skill in Prompting Generative AI Tools (Prompt Engineering) - nurtures the creative aspect of working with generative AI, allowing individuals to tailor personalized outputs to specific objectives or creative efforts.
   • In the context of image generation, the utilization of descriptive language, understanding the trade-offs between creativity and specificity, the possibility of segmenting longer prompts into smaller units, or the incorporation of negative words, can all lead to outputs that are different from the conventional results.
8. Ability to Program and Fine-tune - Provides the technical know-how necessary for the customization and optimization of generative AI models to suit specific needs.
   • This ability is valuable for developing generative AI in specialized domains such as medical writing or dealing with languages other than English. Developers can adapt these models to equip them with a deeper understanding of domain-specific vocabulary and intricacies, leading to more impactful and tailored content generation or for particular linguistic nuances, cultural references, and colloquialisms.
9. Knowledge of the Contexts Where Generative AI is Used - Understand the diverse applications and limitations of generative AI across situations, institutions, and professions to assess the appropriateness of using generative AI tools.
   • Several universities have developed guidelines for the use of LLMs in assignments [26]. This differs from campus to campus, with some of them prohibiting LLMs altogether, and others allowing them for certain types of coursework. Students should learn these guidelines, and behave accordingly.
10. Knowledge of the Ethical Implications - Augments technical proficiency with ethical considerations and inculcates a sense of responsibility by making individuals aware of the ethical considerations tied to the use of generative AI.
11. Knowledge of Legal Aspects - Addresses legal dimensions, ensuring individuals operate within the bounds of intellectual property and other legal frameworks associated with the use of generative AI.
12. Ability to Continuously Learn - Promotes a mindset of continuous learning to stay updated with evolving generative AI technologies, methodologies, and ethical considerations.