This section covers practical tips for using AI tools effectively and responsibly in your studies and daily life. Knowing how to use AI tools can enhance your learning and productivity. Here we focus on general skills that apply across many AI applications (text generators, image creators, etc.), with a focus on generative AI and LLMs.
AI tools powered by LLMs are a general technology with many applications, from writing assistants and chatbots (for text) to image generators, coding assistants, and more. First, identify what you need. Do you want help brainstorming ideas for an essay, or are you trying to solve a coding problem? Do you need an image for a presentation? An AI tool’s effectiveness is often domain-specific, so don’t force a tool to do something it isn’t designed for.
This is a list of tools developed by the FLC AI Resource Group with recommendations about how to use them.
The results you get from AI are only as good as the question or prompt you give. Learning to write clear, specific prompts is a key skill (sometimes called prompt engineering). For example, instead of asking a vague question like “Explain photosynthesis”, you might prompt: “Explain the process of photosynthesis in simple terms for a college freshman biology student.” Providing context, desired detail level, or format can guide the AI to produce more useful output. If the first answer isn’t great, iterate on your prompt. Try adding details (“give 3 key points” or “in 200 words or less”) or ask follow-up questions. Treat it as a conversation where you refine what you’re asking for. Be concise and specific with prompts. It often yields better results
The CLEAR five characteristic Framework was created by Dr. Leo Lo, Dean of the College of University Libraries & Learning Sciences at the University of New Mexico.
| Concise | Logical | Explicit | Adaptive | Reflective |
| Brevity and clarity in prompts | Structured and coherent prompts (response comes out how you specify) |
Clear output specifications (set tone – professional, provide examples) |
Flexibility and customization in prompts | Continuous evaluation and improvement of prompts |
| Replace: “Please provide me with an extensive discussion on the factors that contributed to the economic growth of China during the last few decades” |
Not structured: "Compare the scientific method with the non-scientific method." |
Replace: “Tell me about the French Revolution,” | Replace: “Discuss the impact of social media on mental health” | After acquiring an AI-generated list of strategies for effective time management, evaluate the relevance and applicability of each strategy. |
| Concise: “Identify factors behind China’s recent economic growth.” |
Logical: “Describe the steps in the scientific method, starting with forming a hypothesis and ending with drawing conclusions.” |
Explicit: “Provide a concise overview of the French Revolution, emphasizing its causes, major events, and consequences.” | Adaptive: “Examine the relationship between social media usage and anxiety in adolescents.” |
Consider the target audience’s needs, and use this information to tailor future prompts to generate content that better addresses specific challenges or contexts. |
Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship, 49(4).
The Five I's are a conceptual framework for evaluating outputs of LLMs. Before using outputs, evaluate them considering the following.
Incomplete Most language models cannot access the most recently published work
Inconsistent Unable to replicate the same results, for the same question, over time
Incoherent Cannot provide provenance for where they source their information
Illogical Fail to solve problems that are trivial for humans or other “simpler” software
Indulgent Encourage confirmation bias and path dependency rather than critical thinking
Carroll, A. J., & Borycz, J. (2024). Integrating large language models and generative artificial intelligence tools into information literacy instruction. The Journal of Academic Librarianship, 50(4), 102899. https://doi.org/10.1016/j.acalib.2024.102899
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