Tag: fluency

‘Aztecs Read’ initiative sparks fluency, proficiency in Missouri charter
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Dive Brief:
- Guadalupe Centers Middle School in Kansas City, Missouri, increased reading achievement among students in grades 6-8 through its Aztecs Read initiative, despite not having a school librarian.
- The initiative at the Title I charter school where 70% of students identified as English learners helped establish classroom libraries, host author visits and hold reading challenges.
- Schools that lack librarians need to get creative to ensure that they’re adequately nurturing young readers. Nearly 30% of public schools operated without even a part-time librarian in 2020-21, up from 25% in the mid-2010s, according to data from a joint report titled “Schools Without Librarians,” published in 2024 by Antioch University Seattle and the Institute of Museum and Library Services.
Dive Insight:

The need for literacy experts is especially pronounced in charter schools, 70% of which had no librarian in 2020-21, according to the joint report. Of smaller schools with under 200 students, nearly two-thirds lacked librarians, as did about a third of high-poverty schools, the report noted.

Although Guadalupe Centers Middle School lacks a library, in the wake of the school’s recently implemented Aztecs Read initiative, students in 6-8 grades in 2022-23 averaged 3.67 points of Rasch UnIT — or RIT — growth on the NWEA reading assessment, which accelerated to an average of 6.17 RIT points in 2024-25, the school reported.

With about 370 students, Guadalupe Centers aims to serve as a hub for Kansas City’s Hispanic community, said Christopher Leavens, teacher and lead for English/language arts, who led the establishment of Aztecs Read as an initiative to put a library in every classroom along with various related initiatives such as author visits and an online reading log. A multipurpose room had served as a library—but without a staff librarian, possibly for the school’s entire history.

“I’m an English teacher, and I believe so much in students being able to have good literature, and stories that speak to their culture,” said Leavens, who’s been teaching for 14 years and is in his fourth year at Guadalupe Centers. “Independent reading is so valuable in developing their reading and writing, and in developing who they are, as people — communications skills, empathy, connection. I felt it was important for me to build that up.”

The initiative began during Leavens’ first year at the school, solely in his 7th grade classroom. But toward the end of the school year, he decided that the entire school, which has a total of nine classrooms devoted to students in various places along the English-language journey, should have a dedicated classroom library, and his colleagues, school leaders and the district stepped up to help. For those in the early stages of English language development, the books are in Spanish, but as they advance along the continuum, they move to English-language reading.

“We built it out over time,” he said. “Beyond getting books in the kids’ hands, we’ve tried to build up a reading culture. … Our district has been incredibly supportive of us, financially, to invest in the classroom libraries and build it out.”

Beyond the classroom libraries, Aztecs Read has hosted author visits — including with Pedro Martín, who wrote the graphic memoir “Mexikid.”

“He was able to come in, give a presentation, sign books and have lunch with our kids,” Leavens said.

The program has also invested in the online reading log Beanstack, which enables students to track their reading progress. “It can provide more infrastructure, accountability and build out reading to the home and get parents involved,” he said.

That tracking capability led to “Book of the Break” challenges, during which the school provides different tiers of incentives for students to read over school breaks for at least 10 minutes every day, Leavens said. Those who do enter a drawing for the grand prize, a pair of headphones; or the second prize, one of 10 gift cards to AMC Theatres, which were donated after a teacher reached out to the company.

At the very least, students — who typically wear uniforms — get to enjoy a dress-down day, he said.

“Not all kids are in homes where they have books, or reading role models,” Leavens said. “Getting kids to read is not always easy to do. … The team we have, too, is so resourceful and hard-working. We still have a lot of work to do, but we’ve been able to build up the amount these kids are reading for fun, and their enjoyment when they have read.”
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November 26, 2025
The AI Teammate: Three Roles to Build Student AI Fluency – Faculty Focus
AI is now embedded in teaching and learning. As educators, how do we help students benefit from AI without slipping into dependency, surface-level work, or ethical misconduct? I’ve found a helpful way to clarify conversations with students (and with myself) by thinking of AI as a teammate (or teammates) with clearly defined roles: the Tasker, the Draftsmith, and the Facilitator.

Imagine a meeting of a team of people: someone handles logistics, someone sketches ideas, someone pushes discussion. Framing AI this way has made it easier to talk about students remaining responsible for how and to what purposes they use AI. My approach draws on recent scholarship about AI reshaping how teams work (Dell’Acqua et al., 2025).

The AI Teammates

The Tasker

The Tasker deals with repetitive or procedural work such as managing our calendars, formatting citations, or cleaning datasets. These tasks often are tedious but essential and using AI frees time that can be focused on deeper thinking. To use a Tasker, we can set up a Robotic Process Automation (RPA) with AI, use embedded AI tools such as Copilot in MS:Word or Claude in MS:Excel or Google sheets, or create what is currently referred to as an LLM “Agent.” As Farri & Rosani (2025) describe in their HBR guide to generative AI for managers, we can use AI to lighten the load on procedural chores, affording us the time and energy to engage more with deeper thinking and learning.

Because AI output may contain mistakes, students must learn to identify the levels of risks they believe are acceptable for the given task before turning to AI and must validate its output. The level of validation required may depend on the risk level, needed accuracy and quality, and current knowledge of the tool’s metrics.

We approach the Tasker differently than we do the Draftsmith and Facilitator: we can set our RPA or Agent up to run and only involve ourselves in its applications until we’re sure it can work independently and accurately, or we can trigger the Tasker each time we need it. Students must learn to set aside sufficient validation time before designing a Tasker.

The Draftsmith

The Draftsmith can spot passive voice in students’ writing and explain why it’s problematic, convert an outline into a draft of a PowerPoint, and produce study tools such as multiple choice or essay questions, podcasts, and videos from their course notes. My engineering students have prompted their Draftsmiths using a prompt library I created specifically for them to help them convert their design clients’ stated needs into project requirements. Bussgang (2025) has pointed out how small steps like these can dramatically boost productivity without sacrificing learning.

While AI may generate text or ideas, the final product must be the student’s own (Duffy, 2025). I teach my students that, much to their dismay, they must develop their own writers’ voice before using AI and that this process can take years and many writing efforts! Our writer’s voice presents us to our bosses, colleagues, and clients given that much of our communication at work is through emails. College is the best time to invest in this effort because it’s rare to get opportunities later to do so.

From students’ comments, I believe they use AI in the Draftsmith role more frequently than the other two roles. Few realize the Draftsmith increases cognitive load when they use it correctly and decreases their learning when they use it incorrectly!

It increases the load because they must provide the AI with significant context for it to provide anything other than banal, common prose that flattens their own voice (Purohit, 2025) or “creative” prose that is meaningless. AI use decreases their learning when they use it to write first and second drafts because writing sharpens our thinking and embeds learned concepts into our long-term memory.

The Facilitator

Instead of using AI to generate content (the Draftsmith), the Facilitator is a use of AI as a thinking partner (Solis, 2024 and HBSP’s ManageMentor course 2025). A student might ask AI to play devil’s advocate, suggest counterarguments, or pose probing questions when their paper’s argument needs strengthening. AI might help simulate a stakeholder’s perspective with whom the student role plays to prepare for a meeting or consider how their ideas might land with their student group peers. My students have used AI in the Facilitator role to pressure test their design ideas, review their research plans, and prepare to give presentations. When students use the Facilitator, they’re reflecting (Harbridge, 2025) and thereby deepening their learning.

To get the best value from AI as a Facilitator, the student must approach it with the expectation that it will take considerable time. The Facilitator provides meaningful interaction only when the student gives it their full attention, asks probing questions, provides significant context, and is patient. They may need to iterate, restating their prompts or providing more information, and to ask the LLM to be candid and critical. They may need to step away from the LLM, perhaps try a different one or come back on another day, when it goes off in the wrong direction.

We have become too used to scanning an LLM’s output. Just as I would not expect a student who came to me for career advice to start scrolling on their phone while I’m talking, I must teach them to give their full attention to the Facilitator.

What I’m suggesting here is not a list of use cases. Instead, I’m providing students with an approach to using AI where they plan before typing into the chat box, see its best use as a Facilitator that partners with them in learning and development, and recognize that validation and reading output will be time and cognitively intensive.

What This Means for Teaching

I’ve found that when I teach these roles to students before assigning homework, they make better choices regarding when and how to use AI. Here are some tactics I’ve tried that have netted positive results:
- Design assignments that invite different roles. For example, ask students to begin with AI as a Tasker (perhaps organizing sources or cleaning a dataset), then shift to using the Draftsmith to help them find passive voice or other syntax issues in a paper, and finally draw on the Facilitator to find the holes in their arguments before submitting a final paper. Intentionally stating the role each assignment calls for and requiring them to state when they’re using the Tasker, Draftsmith, or Facilitator solidifies their understanding that their approach to AI differs by role.
- Be clear about where AI helps and where it doesn’t. AI can help with structure, suggestions, and ideation, but it cannot replace revision, critical thinking, or the development of one’s own voice. This must be intentionally taught by including questions requiring students to reflect on what worked and where the AI led them astray in any assignments allowing AI use.
- Encourage disclosure. I talk extensively about “Total AI Transparency”, ensuring students that I will note any use of AI in my communications with them and I expect the same from them. Trust has broken down as both students and instructors believe the other is using AI when they’re not and failing to recognize when they are. Owning up to my own use and being clear on why I used AI for that task encourages students to do the same.
Closing Thoughts

We need to steer students away from extremes of either refusing to use AI at all or letting it rob them of learning. By distinguishing the roles of Tasker, Draftsmith, and Facilitator, we clarify our expectations of students. More importantly, we help them become more deliberate in their approach, preparing them to be AI-fluent.

Illysa Izenberg is an Associate Teaching Professor for the Center for Leadership Education in the Whiting School of Engineering at The Johns Hopkins University. She has been teaching management and business ethics to graduate and undergraduate students via Face-to-Face, online, and blended courses since 2006. Izenberg earned her MBA from the Harvard Graduate School of Business and is the winner of both the Alumni and Pond Excellence in Teaching Awards (2016 and 2020 respectively).

References

Bussgang, J. (2025, May 20). 3 small steps to 10x your productivity with AI this week. The Experimentation Machine. https://experimentationmachine.com/p/10x-this-week

Duffy, A. (2025, April 11). Your CEO just said ‘Use AI or else.’ Here’s what to do next. Every. https://every.to/p/your-ceo-just-said-use-ai-or-else-here-s-what-to-do-next

Farri, E., & Rosani, G. (2025). HBR guide to generative AI for managers. Harvard Business Review Press.

Gross, A. (2025). AI Crash Course [Online course]. SectionAI. https://www.sectionschool.com/courses/ai-crash-course-workshop

Harbridge, R. (2025). AI for Team Leaders [Online course]. SectionAI.

Harvard Business Publishing. (2025). Help your team harness generative AI (Lesson 1). In Leading with generative AI [Online course]. Harvard ManageMentor.

Purohit, R. (Host). (2025, June 18). The man inside the minds of the people building AGI [Audio podcast episode]. In AI & I. Every.

Solis, B. (2024, November). Train your brain to work creatively with Gen AI. Harvard Business School Publishing.
Source link
November 17, 2025
The AI Teammate: Three Roles to Build Student AI Fluency – Faculty Focus
AI is now embedded in teaching and learning. As educators, how do we help students benefit from AI without slipping into dependency, surface-level work, or ethical misconduct? I’ve found a helpful way to clarify conversations with students (and with myself) by thinking of AI as a teammate (or teammates) with clearly defined roles: the Tasker, the Draftsmith, and the Facilitator.

Imagine a meeting of a team of people: someone handles logistics, someone sketches ideas, someone pushes discussion. Framing AI this way has made it easier to talk about students remaining responsible for how and to what purposes they use AI. My approach draws on recent scholarship about AI reshaping how teams work (Dell’Acqua et al., 2025).

The AI Teammates

The Tasker

The Tasker deals with repetitive or procedural work such as managing our calendars, formatting citations, or cleaning datasets. These tasks often are tedious but essential and using AI frees time that can be focused on deeper thinking. To use a Tasker, we can set up a Robotic Process Automation (RPA) with AI, use embedded AI tools such as Copilot in MS:Word or Claude in MS:Excel or Google sheets, or create what is currently referred to as an LLM “Agent.” As Farri & Rosani (2025) describe in their HBR guide to generative AI for managers, we can use AI to lighten the load on procedural chores, affording us the time and energy to engage more with deeper thinking and learning.

Because AI output may contain mistakes, students must learn to identify the levels of risks they believe are acceptable for the given task before turning to AI and must validate its output. The level of validation required may depend on the risk level, needed accuracy and quality, and current knowledge of the tool’s metrics.

We approach the Tasker differently than we do the Draftsmith and Facilitator: we can set our RPA or Agent up to run and only involve ourselves in its applications until we’re sure it can work independently and accurately, or we can trigger the Tasker each time we need it. Students must learn to set aside sufficient validation time before designing a Tasker.

The Draftsmith

The Draftsmith can spot passive voice in students’ writing and explain why it’s problematic, convert an outline into a draft of a PowerPoint, and produce study tools such as multiple choice or essay questions, podcasts, and videos from their course notes. My engineering students have prompted their Draftsmiths using a prompt library I created specifically for them to help them convert their design clients’ stated needs into project requirements. Bussgang (2025) has pointed out how small steps like these can dramatically boost productivity without sacrificing learning.

While AI may generate text or ideas, the final product must be the student’s own (Duffy, 2025). I teach my students that, much to their dismay, they must develop their own writers’ voice before using AI and that this process can take years and many writing efforts! Our writer’s voice presents us to our bosses, colleagues, and clients given that much of our communication at work is through emails. College is the best time to invest in this effort because it’s rare to get opportunities later to do so.

From students’ comments, I believe they use AI in the Draftsmith role more frequently than the other two roles. Few realize the Draftsmith increases cognitive load when they use it correctly and decreases their learning when they use it incorrectly!

It increases the load because they must provide the AI with significant context for it to provide anything other than banal, common prose that flattens their own voice (Purohit, 2025) or “creative” prose that is meaningless. AI use decreases their learning when they use it to write first and second drafts because writing sharpens our thinking and embeds learned concepts into our long-term memory.

The Facilitator

Instead of using AI to generate content (the Draftsmith), the Facilitator is a use of AI as a thinking partner (Solis, 2024 and HBSP’s ManageMentor course 2025). A student might ask AI to play devil’s advocate, suggest counterarguments, or pose probing questions when their paper’s argument needs strengthening. AI might help simulate a stakeholder’s perspective with whom the student role plays to prepare for a meeting or consider how their ideas might land with their student group peers. My students have used AI in the Facilitator role to pressure test their design ideas, review their research plans, and prepare to give presentations. When students use the Facilitator, they’re reflecting (Harbridge, 2025) and thereby deepening their learning.

To get the best value from AI as a Facilitator, the student must approach it with the expectation that it will take considerable time. The Facilitator provides meaningful interaction only when the student gives it their full attention, asks probing questions, provides significant context, and is patient. They may need to iterate, restating their prompts or providing more information, and to ask the LLM to be candid and critical. They may need to step away from the LLM, perhaps try a different one or come back on another day, when it goes off in the wrong direction.

We have become too used to scanning an LLM’s output. Just as I would not expect a student who came to me for career advice to start scrolling on their phone while I’m talking, I must teach them to give their full attention to the Facilitator.

What I’m suggesting here is not a list of use cases. Instead, I’m providing students with an approach to using AI where they plan before typing into the chat box, see its best use as a Facilitator that partners with them in learning and development, and recognize that validation and reading output will be time and cognitively intensive.

What This Means for Teaching

I’ve found that when I teach these roles to students before assigning homework, they make better choices regarding when and how to use AI. Here are some tactics I’ve tried that have netted positive results:
- Design assignments that invite different roles. For example, ask students to begin with AI as a Tasker (perhaps organizing sources or cleaning a dataset), then shift to using the Draftsmith to help them find passive voice or other syntax issues in a paper, and finally draw on the Facilitator to find the holes in their arguments before submitting a final paper. Intentionally stating the role each assignment calls for and requiring them to state when they’re using the Tasker, Draftsmith, or Facilitator solidifies their understanding that their approach to AI differs by role.
- Be clear about where AI helps and where it doesn’t. AI can help with structure, suggestions, and ideation, but it cannot replace revision, critical thinking, or the development of one’s own voice. This must be intentionally taught by including questions requiring students to reflect on what worked and where the AI led them astray in any assignments allowing AI use.
- Encourage disclosure. I talk extensively about “Total AI Transparency”, ensuring students that I will note any use of AI in my communications with them and I expect the same from them. Trust has broken down as both students and instructors believe the other is using AI when they’re not and failing to recognize when they are. Owning up to my own use and being clear on why I used AI for that task encourages students to do the same.
Closing Thoughts

We need to steer students away from extremes of either refusing to use AI at all or letting it rob them of learning. By distinguishing the roles of Tasker, Draftsmith, and Facilitator, we clarify our expectations of students. More importantly, we help them become more deliberate in their approach, preparing them to be AI-fluent.

Illysa Izenberg is an Associate Teaching Professor for the Center for Leadership Education in the Whiting School of Engineering at The Johns Hopkins University. She has been teaching management and business ethics to graduate and undergraduate students via Face-to-Face, online, and blended courses since 2006. Izenberg earned her MBA from the Harvard Graduate School of Business and is the winner of both the Alumni and Pond Excellence in Teaching Awards (2016 and 2020 respectively).

References

Bussgang, J. (2025, May 20). 3 small steps to 10x your productivity with AI this week. The Experimentation Machine. https://experimentationmachine.com/p/10x-this-week

Duffy, A. (2025, April 11). Your CEO just said ‘Use AI or else.’ Here’s what to do next. Every. https://every.to/p/your-ceo-just-said-use-ai-or-else-here-s-what-to-do-next

Farri, E., & Rosani, G. (2025). HBR guide to generative AI for managers. Harvard Business Review Press.

Gross, A. (2025). AI Crash Course [Online course]. SectionAI. https://www.sectionschool.com/courses/ai-crash-course-workshop

Harbridge, R. (2025). AI for Team Leaders [Online course]. SectionAI.

Harvard Business Publishing. (2025). Help your team harness generative AI (Lesson 1). In Leading with generative AI [Online course]. Harvard ManageMentor.

Purohit, R. (Host). (2025, June 18). The man inside the minds of the people building AGI [Audio podcast episode]. In AI & I. Every.

Solis, B. (2024, November). Train your brain to work creatively with Gen AI. Harvard Business School Publishing.
Source link
November 17, 2025
3 strategies to boost student reading fluency this school year

Key points:

With the new school year now rolling, teachers and school leaders are likely being hit with a hard truth: Many students are not proficient in reading.

This, of course, presents challenges for students as they struggle to read new texts and apply what they are learning across all subject areas, as well as for educators who are diligently working to support students’ reading fluency and overall academic progress.

Understanding the common challenges students face with reading–and knowing which instructional strategies best support their growth–can help educators more effectively get students to where they need to be this school year.

Understanding the science of learning

Many districts across the country have invested in evidence-based curricula grounded in the science of reading to strengthen how foundational skills such as decoding and word recognition are taught. However, for many students, especially those receiving Tier 2 and Tier 3 interventions, this has not been enough to help them develop the automatic word recognition needed to become fluent, confident readers.

This is why coupling the science of reading with the science of learning is so important when it comes to reading proficiency. Simply stated, the science of learning is how students learn. It identifies the conditions needed for students to build automaticity and fluency in complex skills, and it includes principles such as interleaving, spacing practice, varying tasks, highlighting contrasts, rehearsal, review, and immediate feedback–all of which are essential for helping students consolidate and generalize their reading skills.

When these principles are intentionally combined with the science of reading’s structured literacy principles, students are able to both acquire new knowledge and retain, retrieve, and apply it fluently in new contexts.

Implementing instructional best practices

The three best practices below not only support the use of the science of learning and the science of reading, but they give educators the data and information needed to help set students up for reading success this school year and beyond.

Screen all students. It is important to identify the specific strengths and weaknesses of each student as early as possible so that educators can personalize their instruction accordingly.

Some students, even those in upper elementary and middle school, may still lack foundational skills, such as decoding and automatic word recognition, which in turn negatively impact fluency and comprehension. Using online screeners that focus on decoding skills, as well as automatic word recognition, can help educators more quickly understand each student’s needs so they can efficiently put targeted interventions in place to help.

Online screening data also helps educators more effectively communicate with parents, as well as with a student’s intervention team, in a succinct and timely way.

Provide personalized structured, systematic practice. This type of practice has been shown to help close gaps in students’ foundational skills so they can successfully transfer their decoding and automatic word recognition skills to fluency. The use of technology and online programs can optimize the personalization needed for students while providing valuable insights for teachers.

Of course, when it comes to personalizing practice, technology should always enhance–not replace–the role of the teacher. Technology can help differentiate the questions and lessons students receive, track students’ progress, and engage students in a non-evaluative learning environment. However, the personal attention and direction given by a teacher is always the most essential aid, especially for struggling readers.

Monitor progress on oral reading. Practicing reading aloud is important for developing fluency, although it can be very personal and difficult for many struggling learners. Students may get nervous, embarrassed, or lose their confidence. As such, the importance of a teacher’s responsiveness and ongoing connection while monitoring the progress of a student cannot be overstated.

When teachers establish the conditions for a safe and trusted environment, where errors can occur without judgment, students are much more motivated to engage and read aloud. To encourage this reading, teachers can interleave passages of different lengths and difficulty levels, or revisit the same text over time to provide students with spaced opportunities for practice and retrieval. By providing immediate and constructive feedback, teachers can also help students self-correct and refine their skills in real time.

Having a measurable impact

All students can become strong, proficient readers when they are given the right tools, instruction, and support grounded in both the science of learning and the science of reading. For educators, this includes screening effectively, providing structured and personalized practice, and creating environments where students feel comfortable learning and practicing skills and confident reading aloud.

By implementing these best practices, which take into account both what students need to learn and how they learn best, educators can and will make a measurable difference in students’ reading growth this school year.

Dr. Carolyn Brown, Foundations in Learning

Dr. Carolyn Brown is the co-founder and chief academic officer of Foundations in Learning, creator of WordFlight. Dr. Brown has devoted her career to ongoing research and development that targets underlying learning processes to optimize language and literacy development for all students.

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October 31, 2025