Category: instruction

  • Embracing complexity in writing instruction

    Embracing complexity in writing instruction

    Key points:

    Early in our careers, when we were fresh-faced and idealistic (we still are!) the prepackaged curriculum and the advice of more experienced colleagues was the go-to resource. Largely, we were advised that teaching writing was a simple matter of having students walk through and complete organizers, spending about one day for each “stage” of the writing process. At the end of the writing unit, students had finished their compositions–the standardized, boring, recreated ideas that we taught them to write.

    As we matured and grew as teachers of writing, we learned that teaching writing in such simplistic ways may be easier, but it was not actually teaching students to be writers. We learned with time and experience that writing instruction is a complex task within a complex system.

    Complex systems and wicked problems

    Complexity as it is applied to composition instruction recognizes that there is more than just a linear relationship between the student, the teacher, and the composition. It juggles the experiences of individual composers, characteristics of genre, availability of resources, assignment and individual goals, and constraints of composing environments. As with other complex systems and processes, it is non-linear, self-organizing, and unpredictable (Waltuck, 2012).

    Complex systems are wicked in their complexity; therefore, wicked problems cannot be solved by simple solutions. Wicked problems are emergent and generative; they are nonlinear as they do not follow a straight path or necessarily have a clear cause-and-effect relationship. They are self-organizing, evolving and changing over time through the interactions of various elements. They are unpredictable and therefore difficult to anticipate how they will unfold or what the consequences of any intervention might be. Finally, they are often interconnected, as they are symptoms of other problems. In essence, a wicked problem is a complex issue embedded in a dynamic system (Rittel & Webber, 1973).

    Writing formulas are wicked

    As formulaic writing has become and remains prevalent in instruction and classroom writing activity, graphic organizers and structural guides, which were introduced as a tool to support acts of writing, have become a wicked problem of formula; the resource facilitating process has become the focus of product. High-stakes standardized assessment has led to a focus on compliance, production, and quality control, which has encouraged the use of formulas to simplify and standardize writing instruction, the student writing produced, and the process of evaluation of student work. Standardization may improve test scores in certain situations, but does not necessarily improve learning. Teachers resort to short, formulaic writing to help students get through material more quickly as well as data and assessment compliance. This serves to not only create product-oriented instruction, but a false dichotomy between process and product, ignoring the complex thinking and design that goes into writing.

    As a result of such a narrow view of and limited focus on writing process and purpose, formulas have been shown to constrain thinking and limit creativity by prioritizing product over the composing process. The five-paragraph essay, specifically, is a structure that hinders authentic composing because it doesn’t allow for the “associative leaps” between ideas that come about in less constrained writing. Formulas undermine student agency by limiting writers’ abilities to express their unique voices because of over-reliance on rigid structures (Campbell, 2014; Lannin & Fox, 2010; Rico, 1988).

    An objective process lens: A wicked solution

    The use of writing formulas grew from a well-intentioned desire to improve student writing, but ultimately creates a system that is out of balance, lacking the flexibility to respond to a system that is constantly evolving. To address this, we advocate for shifting away from rigid formulas and towards a design framework that emphasizes the individual needs and strategies of student composers, which allows for a more differentiated approach to teaching acts of writing.

    The proposed framework is an objective process lens that is informed by design principles. It focuses on the needs and strategies that drive the composing process (Sharples, 1999). This approach includes two types of needs and two types of strategies:

    • Formal needs: The assigned task itself
    • Informal needs: How a composer wishes to execute the task
    • “What” strategies: The concrete resources and available tools
    • “How” strategies: The ability to use the tools

    An objective process lens acknowledges that composing is influenced by the unique experiences composers bring to the task. It allows teachers to view the funds of knowledge composers bring to a task and create entry points for support.

    The objective process lens encourages teachers to ask key questions when designing instruction:

    • Do students have a clear idea of how to execute the formal need?
    • Do they have access to the tools necessary to be successful?
    • What instruction and/or supports do they need to make shifts in ideas when strategies are not available?
    • What instruction in strategies is necessary to help students communicate their desired message effectively?

    Now how do we do that?

    Working within a design framework that balances needs and strategies starts with understanding the type of composers you are working with. Composers bring different needs and strategies to each new composing task, and it is important for instructors to be aware of those differences. While individual composers are, of course, individuals with individual proclivities and approaches, we propose that there are (at least) four common types of student composers who bring certain combinations of strategies and needs to the composition process: the experience-limited, the irresolute, the flexible, and the perfectionist composers. By recognizing these common composer types, composition instructors can develop a flexible design for their instruction.

    An experience-limited composer lacks experience in applying both needs and strategies to a composition, so they are entirely reliant on the formal needs of the assigned task and any what-strategies that are assigned by the instructor. These students gravitate towards formulaic writing because of their lack of experience with other types of writing. Relatedly, an irresolute composer may have a better understanding of the formal and informal needs, but they struggle with the application of what and how strategies for the composition. They can become overwhelmed with options of what without a clear how and become stalled during the composing process. Where the irresolute composer becomes stalled, the flexible composer is more comfortable adapting their composition. This type of composer has a solid grasp on both the formal and informal needs and is willing to adapt the informal needs as necessary to meet the formal needs of the task. As with the flexible composer, the perfectionist composer is also needs-driven, with clear expectations for the formal task and their own goals for the informal tasks. Rather than adjusting the informal needs as the composition develops, a perfectionist composer will focus intensely on ensuring that their final product exactly meets their formal and informal needs.

    Teaching writing requires embracing its complexity and moving beyond formulaic approaches prioritizing product over process. Writing is a dynamic and individualized task that takes place within a complex system, where composers bring diverse needs, strategies, and experiences. By adopting a design framework, teachers of writing and composing can support students in navigating this complexity, fostering creativity, agency, and authentic expression. It is an approach that values funds of knowledge students bring to the writing process, recognizing the interplay of formal and informal needs, as well as their “what” and “how” strategies; those they have and those that need growth via instruction and experience. Through thoughtful design, we can grow flexible, reflective, and skilled communicators who are prepared to navigate the wicked challenges of composing in all its various forms.

    These ideas and more can be found in When Teaching Writing Gets Tough: Challenges and Possibilities in Secondary Writing Instruction.

    References

    Campbell, K. H. (2014). Beyond the five-paragraph essay. Educational Leadership, 71(7), 60-65.

    Lannin, A. A., & Fox, R. F. (2010). Chained and confused: Teacher perceptions of formulaic writing. Writing & Pedagogy, 2(1), 39-64.

    Rico, G. L. (1988). Against formulaic writing. The English Journal, 77(6), 57-58.

    Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169.

    Sharples, M. (1999). How we write : writing as creative design (1st ed.). Routledge. https://doi.org/10.4324/9780203019900

    Waltuck, B. A. (2012). Characteristics of complex systems. The Journal for Quality & Participation, 34(4), 13–15.

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  • Helping students evaluate AI-generated content

    Helping students evaluate AI-generated content

    Key points:

    Finding accurate information has long been a cornerstone skill of librarianship and classroom research instruction. When cleaning up some materials on a backup drive, I came across an article I wrote for the September/October 1997 issue of Book Report, a journal directed to secondary school librarians. A generation ago, “asking the librarian” was a typical and often necessary part of a student’s research process. The digital tide has swept in new tools, habits, and expectations. Today’s students rarely line up at the reference desk. Instead, they consult their phones, generative AI bots, and smart search engines that promise answers in seconds. However, educators still need to teach students the ability to be critical consumers of information, whether produced by humans or generated by AI tools.

    Teachers haven’t stopped assigning projects on wolves, genetic engineering, drug abuse, or the Harlem Renaissance, but the way students approach those assignments has changed dramatically. They no longer just “surf the web.” Now, they engage with systems that summarize, synthesize, and even generate research responses in real time.

    In 1997, a keyword search might yield a quirky mix of werewolves, punk bands, and obscure town names alongside academic content. Today, a student may receive a paragraph-long summary, complete with citations, created by a generative AI tool trained on billions of documents. To an eighth grader, if the answer looks polished and is labeled “AI-generated,” it must be true. Students must be taught how AI can hallucinate or simply be wrong at times.

    This presents new challenges, and opportunities, for K-12 educators and librarians in helping students evaluate the validity, purpose, and ethics of the information they encounter. The stakes are higher. The tools are smarter. The educator’s role is more important than ever.

    Teaching the new core four

    To help students become critical consumers of information, educators must still emphasize four essential evaluative criteria, but these must now be framed in the context of AI-generated content and advanced search systems.

    1. The purpose of the information (and the algorithm behind it)

    Students must learn to question not just why a source was created, but why it was shown to them. Is the site, snippet, or AI summary trying to inform, sell, persuade, or entertain? Was it prioritized by an algorithm tuned for clicks or accuracy?

    A modern extension of this conversation includes:

    • Was the response written or summarized by a generative AI tool?
    • Was the site boosted due to paid promotion or engagement metrics?
    • Does the tool used (e.g., ChatGPT, Claude, Perplexity, or Google’s Gemini) cite sources, and can those be verified?

    Understanding both the purpose of the content and the function of the tool retrieving it is now a dual responsibility.

    2. The credibility of the author (and the credibility of the model)

    Students still need to ask: Who created this content? Are they an expert? Do they cite reliable sources? They must also ask:

    • Is this original content or AI-generated text?
    • If it’s from an AI, what sources was it trained on?
    • What biases may be embedded in the model itself?

    Today’s research often begins with a chatbot that cannot cite its sources or verify the truth of its outputs. That makes teaching students to trace information to original sources even more essential.

    3. The currency of the information (and its training data)

    Students still need to check when something was written or last updated. However, in the AI era, students must understand the cutoff dates of training datasets and whether search tools are connected to real-time information. For example:

    • ChatGPT’s free version (as of early 2025) may only contain information up to mid-2023.
    • A deep search tool might include academic preprints from 2024, but not peer-reviewed journal articles published yesterday.
    • Most tools do not include digitized historical data that is still in manuscript form. It is available in a digital format, but potentially not yet fully useful data.

    This time gap matters, especially for fast-changing topics like public health, technology, or current events.

    4. The wording and framing of results

    The title of a website or academic article still matters, but now we must attend to the framing of AI summaries and search result snippets. Are search terms being refined, biased, or manipulated by algorithms to match popular phrasing? Is an AI paraphrasing a source in a way that distorts its meaning? Students must be taught to:

    • Compare summaries to full texts
    • Use advanced search features to control for relevance
    • Recognize tone, bias, and framing in both AI-generated and human-authored materials

    Beyond the internet: Print, databases, and librarians still matter

    It is more tempting than ever to rely solely on the internet, or now, on an AI chatbot, for answers. Just as in 1997, the best sources are not always the fastest or easiest to use.

    Finding the capital of India on ChatGPT may feel efficient, but cross-checking it in an almanac or reliable encyclopedia reinforces source triangulation. Similarly, viewing a photo of the first atomic bomb on a curated database like the National Archives provides more reliable context than pulling it from a random search result. With deepfake photographs proliferating the internet, using a reputable image data base is essential, and students must be taught how and where to find such resources.

    Additionally, teachers can encourage students to seek balance by using:

    • Print sources
    • Subscription-based academic databases
    • Digital repositories curated by librarians
    • Expert-verified AI research assistants like Elicit or Consensus

    One effective strategy is the continued use of research pathfinders that list sources across multiple formats: books, journals, curated websites, and trusted AI tools. Encouraging assignments that require diverse sources and source types helps to build research resilience.

    Internet-only assignments: Still a trap

    Then as now, it’s unwise to require students to use only specific sources, or only generative AI, for research. A well-rounded approach promotes information gathering from all potentially useful and reliable sources, as well as information fluency.

    Students must be taught to move beyond the first AI response or web result, so they build the essential skills in:

    • Deep reading
    • Source evaluation
    • Contextual comparison
    • Critical synthesis

    Teachers should avoid giving assignments that limit students to a single source type, especially AI. Instead, they should prompt students to explain why they selected a particular source, how they verified its claims, and what alternative viewpoints they encountered.

    Ethical AI use and academic integrity

    Generative AI tools introduce powerful possibilities including significant reductions, as well as a new frontier of plagiarism and uncritical thinking. If a student submits a summary produced by ChatGPT without review or citation, have they truly learned anything? Do they even understand the content?

    To combat this, schools must:

    • Update academic integrity policies to address the use of generative AI including clear direction to students as to when and when not to use such tools.
    • Teach citation standards for AI-generated content
    • Encourage original analysis and synthesis, not just copying and pasting answers

    A responsible prompt might be: “Use a generative AI tool to locate sources, but summarize their arguments in your own words, and cite them directly.”

    In closing: The librarian’s role is more critical than ever

    Today’s information landscape is more complex and powerful than ever, but more prone to automation errors, biases, and superficiality. Students need more than access; they need guidance. That is where the school librarian, media specialist, and digitally literate teacher must collaborate to ensure students are fully prepared for our data-rich world.

    While the tools have evolved, from card catalogs to Google searches to AI copilots, the fundamental need remains to teach students to ask good questions, evaluate what they find, and think deeply about what they believe. Some things haven’t changed–just like in 1997, the best advice to conclude a lesson on research remains, “And if you need help, ask a librarian.”

    Steven M. Baule, Ed.D., Ph.D.
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  • How 4 districts use AI tools to transform education

    How 4 districts use AI tools to transform education

    Key points:

    • School districts turn to AI to improve personalized education for students
    • With AI coaching, a math platform helps students tackle tough concepts
    • 5 practical ways to integrate AI into high school science
    • For more news on AI in education, visit eSN’s Digital Learning hub

    Simply put, AI can do a lot–it can personalize learning, help students expand on ideas for assignments, and reduce time spent on administrative tasks, freeing up educators to spend more time on instruction.