Some of the early conceptual papers in AI literacy are worth revisiting. Lo’s (2023) “The CLEAR path,” published in The Journal of Academic Librarianship in April 2023, is one of them. The paper came out about five months after ChatGPT launched, when academic librarians were trying to figure out how to teach prompt engineering as a literacy skill. Three years on, the framework still travels surprisingly well, but the gaps in what it covers are also clearer.
The Five Letters
Lo’s CLEAR Framework is a mnemonic for prompt engineering: Concise, Logical, Explicit, Adaptive, Reflective. Each letter names a core principle. Concise calls for stripping prompts of superfluous wording. Logical asks for ideas to follow a natural progression. Explicit specifies the output format and scope. Adaptive treats prompts as drafts that get revised when results miss the target. Reflective closes the loop, with users evaluating outputs and feeding what they learn into future prompts.
The Concise principle gets the cleanest example contrast in the paper. Lo argues that “a concise prompt removes superfluous information, allowing AI language models to focus on the most important aspects of the task, resulting in more pertinent and precise responses” (p. 2). The before-and-after example is “Can you provide me with a detailed explanation of the process of photosynthesis and its significance?” becoming “Explain the process of photosynthesis and its significance.” Less wording, sharper output.
Prompt Engineering CLEAR framework
What I find most useful in CLEAR is the Reflective component. Most prompt engineering advice treats prompting as a one-shot transaction. Lo (2023) emphasizes that “adopting a reflective perspective enables users to evaluate the performance of their AI model based on user feedback and their own assessments, identifying areas for improvement and adjusting their approach accordingly” (p. 2). That’s the meta-skill the framework builds toward: prompt engineering as iterative practice.
The Adaptive principle is also stronger than it looks. The willingness to revise a prompt when it doesn’t land is something many students still don’t do. They write one prompt, see a mediocre output, and accept it. The framework names that as a literacy gap, not a tool problem.
Cheng et al.’s (2025) work on AI questions and writing quality, which I’ve covered before, makes a parallel point: students who ask better questions of generative AI produce better writing. CLEAR gives teachers structured language for what better questions look like.
What Three Years Has Changed
The paper is upfront about its moment. April 2023 was very early. ChatGPT was at version 3.5 for most free-tier users. GPT-4 had just launched the month before. Reasoning models, agentic AI, retrieval-augmented generation, and the modern multimodal tools didn’t exist as mainstream products yet.
Three years on, the technical context is unrecognizable. AI tools handle clarification, ambiguous prompts, and follow-up questions much more elegantly. CLEAR’s emphasis on conciseness now reads as a workaround for limitations the models no longer have.
The other shift is conceptual. Prompt engineering itself is now a smaller piece of AI literacy than it was in 2023. Students need to think about hallucination, bias, source attribution, and ethical use in ways CLEAR doesn’t really address. The 2025 ACRL competencies framework Lo chaired, which I’ve covered before, is built around using AI thoughtfully across an entire information ecology. That’s a much wider lens.
What Teachers Should Take
Even with the temporal gap, three things in CLEAR hold up for classroom teaching.
The mnemonic structure works. Five letters that students can recall under pressure beats a fourteen-page framework. Teachers can use CLEAR as a fast scaffold for prompt engineering segments in K-12 or higher ed AI literacy modules.
The reflection step deserves emphasis in any teaching context. Most students don’t iterate on prompts because they haven’t been taught that iteration is part of the skill. Even two rounds of prompting with explicit reasons for revision is a high-leverage move.
The adaptive principle pairs well with explicit instruction on AI temperature settings, where they’re available. Students need to learn when to ask for tightly focused output and when to invite creative exploration. That’s a calibration capacity worth developing deliberately.
Where I’d Push Further
The paper’s biggest limit is that it’s conceptual without empirical follow-up. Lo proposes the framework based on his own analysis of what makes prompts work. There’s no study showing that students who learn CLEAR write better prompts than students who don’t. That work is still missing in the literature.
A second concern is audience scope. CLEAR is targeted at academic librarians teaching beginners. The framework doesn’t differentiate between novice and intermediate users. A graduate student already comfortable with ChatGPT doesn’t need to start at “be concise.” The framework would benefit from explicit progression for users at different starting points.
The 2024 follow-up survey by Lo, which I’ve covered before, surfaces a related gap. Most academic librarians weren’t using formal frameworks at all when teaching AI. CLEAR didn’t propagate as widely as it could have.
The author concludes that “the CLEAR Framework offers academic librarians a valuable opportunity to enhance their instruction in information literacy and better prepare students for the challenges and opportunities presented by AI-generated content” (p. 3). I agree, with one update. The opportunity is no longer just for librarians. The framework belongs in any classroom where students use generative AI for serious work.
Teach the five letters. Iteration is part of the skill. Students need to know what’s changed since 2023. The framework belongs to all teachers now.
References
- Association of College and Research Libraries. (2025). AI competencies for academic library workers. American Library Association. https://www.ala.org/sites/default/files/2025-10/acrl_ai_competencies.pdf
- Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship, 49, Article 102720. https://doi.org/10.1016/j.acalib.2023.102720
- Lo, L. S. (2024). Evaluating AI literacy in academic libraries: A survey study with a focus on U.S. employees. College & Research Libraries, 85(5). https://crl.acrl.org/index.php/crl/article/view/26409/34344