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LitLab

LitLab is a literacy tool that generates decodable texts matched to the exact phonics skills a class is learning. A teacher picks the skill their students are working on and shares information about their curriculum; LitLab then produces short, illustrated stories built only from the sounds and words students have been taught, so students can practice reading what they know. Students read the stories on their own or aloud—solo or as a class—and can generate their own stories using the same skills.

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What it looks like in action

What the students do:

Students read the decodable assigned for the specific lesson and skill from the Tier 1 or Tier 2 program that the class is working on. They can read silently, with a partner, aloud while recording themselves, or together as a whole class with the teacher leading. As they read, students answer comprehension questions about the story and practice spelling target words pulled from the text, getting instant feedback on whether they got the words correct. Once students finish their assigned reading, they can generate their own decodable stories that help them practice the same skills, choosing things like the character and setting.

What the AI does:

LitLab generates decodable stories, building each one to match a specific point in a phonics program's scope and sequence. Rather than an open-ended language model, it runs on a rule-based engine that breaks words into their sound-and-letter parts and assembles stories using only the skills a student has been taught, so the text stays decodable. It generates an illustration to go with each story, and can build a story around characters, settings, or vocabulary that the teacher or student chooses. If students choose to read aloud and record themselves, a separate speech-recognition tool analyzes the recording, comparing what it hears to the text to flag which words the student got right, which they missed, and how quickly and smoothly they read. When it catches a struggle, it segments the word into phonemes, shows a mouth-and-tongue video for each sound, blends them, then asks the student to do the same. It traces each word a student reads or spells back to the lesson that skill was taught in, and uses oral-reading and spelling accuracy to place each student's progress along the curriculum's scope and sequence. It turns that into fluency and accuracy data for the teacher and checks students' answers on the comprehension questions and spelling tasks, telling the student right away whether each was correct.

What the teacher does:

Teachers choose the phonics skill their class is working on, based on the core curriculum, and assign matching decodables from the dashboard, setting different assignments for different groups or grade levels. When they need a story for a specific skill, they can also generate their own, building it around particular characters, names, or vocabulary. They decide how students will use the stories, as independent or partner reading, as a whole-group story the teacher leads, or as a literacy center students rotate through. They can also decide whether to record student reading for later review. Once students have read, teachers can review the dashboard data, which shows completion, comprehension, and pre-reading skill data and, when recording was on, their fluency and accuracy data. They use this to see who has mastered the skill and who needs more practice, to plan small groups, and to decide what to reteach. Teachers can pull up a student's recording during a reading conference to listen back with them, and can lock the create-your-own feature when they want students focused on assigned reading.

What the leaders do:

Leaders adopt LitLab and set the scope of its use across a school or school system. They can set routines around its use and consider scheduling shifts to fit the decodable practice into existing phonics blocks. Leaders can access the admin dashboard to see how reading is progressing across classrooms and grades, help teachers identify where students are struggling, and target coaching and PD around implementation.

Reach

~450,000

Students

~38,000

Teachers

215

Schools

paid schools (including via school systems)

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School systems

Use

Recommended use

Two sessions a week; approximately 15 minutes per session

Actual use

TBD — pending data‍


‍Note: Most of LitLab's reach is viral, free teacher use.

Product dimensions

Subject

Reading/Early literacy

Grade span

K–5

Curriculum alignment

Curriculum-connected: LitLab generates decodables and offers progress monitoring that follow each program's scope and sequence: UFLI, CKLA, Fundations, Reading Horizons, Benchmark, and Superkids

Design scope

Specific instructional job

The way AI interacts with students

  • The AI asks questions / presents prompts students respond to
  • Right/wrong signal (most of the time)
  • AI gives feedback directly to students

Teacher guidance

Nudges specific teacher actions (e.g., dashboard prompts during use)

Social vs. solo use

Solo by default—primarily independent, small groups possible

Teacher override

Teacher can override the AI scoring

Product adoption

Both — teacher-adopted and system-adopted

Dashboard scope

Teacher and leader dashboards available

Dosage recommendations

Provides dosage recommendations

User values icon

Users' take on wins and limitations

There are clear trends in what users (i.e., teachers, students, instructional leaders) see as the wins and limitations of each product. For each trend below, we've included one illustrative quote from our user interviews.

Wins

LitLab gives teachers decodables that align with the phonics program they already teach, so most lessons have matching practice text without having to buy or build it.

"I have some decodable books in my library, but not a complete set, and I have to piecemeal books to the skills I’m teaching. LitLab has been a gift because it's decodable resources that we can use that align with UFLI [University of Florida Literacy Institute’s Curriculum]....Every time I teach a phonics lesson, I'm using the LitLab decodables to back up what they're learning in the curriculum."

— Reading Specialist

When a small group includes students at different skill points, LitLab lets the teacher assign each the story that matches where they are. This differentiates the practice within a single group, so each student works on the skill they're ready for, instead of the whole group reading the same text whether it fits them or not.

"Some of the students are moving so much faster. So in one group, some students might still be reading the skills from last week, whereas other students are now on the current skill that we're working on. So, the way that it's set up really lends beautifully to differentiation."

— Teacher, 2nd Grade

The purpose of a decodable is to move a skill from a rule a student can recite to something they use while reading. Because LitLab's stories are built from the exact skill just taught, students practice it in connected text right after the lesson, which is the step that turns a memorized rule into reading.

"I just love that LitLab gives them practice. It makes that connection. They're retaining what they learned. They're not just memorizing the phonics rule. They're actually applying it with reading."

— Teacher, 1st Grade

Limitations

The oral-reading feature, where students read aloud so the tool can analyze fluency, may be hard to run with a whole class at once. Students reading aloud at the same time distract each other, so in practice the fluency analysis works best in a small group or intervention setting or for homework, rather than for everyone at once.

"If there are five of them reading at one time, they're listening to each other. They're not able to focus on the book in front of them….Even if I spread them out, there's not enough room to spread out everyone."

— Teacher, 2nd Grade

LitLab's comprehension questions are built as Depth of Knowledge (DOK) Level 1 checks for understanding. They are quick, multiple-choice questions that a student can answer without really reading, and the feedback is limited to an affirmation or a prompt to try again. So the tool builds reading volume, but it is not designed to assess inference or depth of understanding.

"The multiple choice is really like, you can just click whatever. You don't have to read the question…and then the response back to one student was, 'you rock’ for a correct answer and for an incorrect answer. The system just kept prompting the student to try again, with no further scaffolding or explanation."

— District Tech Director

LitLab doesn't yet act on assessment data to route students automatically, so differentiating is manual: the teacher decides each student's level and assigns the matching stories every time.

"I can differentiate and assign different stories to my kids, run different levels. But if it had my data in there already, it [could] assign books to students who are on this level or working on this skill."

— Reading Specialist

Our instructional take

Instructional wins

  • Students get more practice reading text built around the letter-sound combination they are working on. LitLab generates a decodable for any point in a phonics program's scope and sequence, so that practice does not depend on which books—or how many— a classroom already owns.
  • When used with the audio recording, students get more feedback on their reading and teachers get more insight into student successes and challenges through word-level analysis of what the student read aloud.
  • LitLab helps teachers address the range of reading progress in their classroom without changing how reading groups are structured: the text differentiates, so teachers do not have to run more groups or give the whole group one text that fits some students and not others. 
  • Progress data comes back mapped to the curriculum rather than as a score. LitLab analyzes a student's reading at the phoneme and grapheme level and traces each miss to the specific lesson in the program's scope and sequence where that skill was taught. Teachers see which sound-spelling patterns to reteach and where those sit in their own sequence, instead of a level or percentage they have to translate into an instructional next step.

Instructional limitations

  • LitLab focuses on decoding and fluency practice through decodable text, but does not incorporate vocabulary or knowledge building unless a teacher assigns a knowledge-building decodable created by LitLab or themselves. It addresses strands of the reading rope but does not attend to several strands that also matter significantly for students’ reading success.
  • LitLab measures a student's reading speed and accuracy on its own decodables, which contain only skills that student has been taught. Scores are reflective of a student's mastery of the skills in the decodable, but not necessarily grade-level proficiency—or whether a student is catching up to grade-level proficiency. 
  • Fluency and accuracy data appears on the teacher dashboard only for students who read with recording on, which in practice means small group, intervention, or homework. For everyone else the teacher gets only completion and comprehension data with no record of how a student’s reading actually sounded.
  • LitLab reports comprehension alongside its decoding data, but the questions are multiple choice that a student could answer without reading the story; incorrect answers return only a prompt to try again.

Implementation considerations

  • LitLab shares all the standard school-based implementation considerations: scheduling (within the class schedule and the scope and sequence for instruction), teacher and leader training, tech set up, and expectations for use.
  • Leaders need to consider how to schedule decodable practice without taking time from core reading instruction. Where LitLab runs as a whole-class activity, it takes teacher-led time and provides no oral reading data, because recording requires students reading aloud without reading over each other.
  • Leaders need to be explicit about how far LitLab's comprehension data goes. A correct multiple choice answer does not establish that a student understood the story. Leaders and teachers reading the dashboard should treat the comprehension column as a prompt to look closer, not as evidence of understanding, and should not use it in place of whatever comprehension assessment the core program already runs.
Evaluation icon

Evidence of Impact

Available evidence

Study underway

There is no independent, causal evidence of impact on student learning yet.

A quasi-experimental study (QED) is underway, comparing classrooms using LitLab against business-as-usual classrooms, and measuring decoding, encoding, and oral reading fluency on the Arkansas state assessment. The study is evaluated by SETA-ED, funded by Accelerate: The National Collaborative for Accelerated Learning and takes place across nine Arkansas elementary schools. The design was pre-registered before data collection. Data collection is complete and analysis is underway, with results expected in fall 2026. 

An earlier feasibility study was conducted in 2024 with Lean Lab Education and Digital Promise.

Study pre-registration, Open Science Framework · Study details provided by LitLab · Leanlab Education (codesign research partner) · AI in Action Evidence Scan (June 2026)

From the developer icon

From the developer

From

LitLab

The following was written by the team at LitLab and has been edited for clarity and tone by Instruction Partners. 

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As of May 2026, LitLab incorporated encoding (spelling) practice data into the skill maps, which is available to students who do not have recording on, so a teacher can track progress aligned to their scope and sequence through that function.

In the coming year, LitLab is focused on deepening instructional coherence, so that formative signal from a student's practice connects directly into what a teacher does next, without a translation step in between. This includes a focus on:

  • Small-group and reteach recommendations: LitLab will surface student-level reteach guidance tied to specific lessons in a school system’s existing Tier 1 program and group students by shared skill gaps for targeted small-group instruction.
  • Automated skill progression: Once a student demonstrates mastery, LitLab auto-assigns the next skill in sequence, removing a manual step from every teacher's week.
  • Clearer data, deeper analysis: We're rebuilding how performance data is presented so patterns are legible at a glance rather than buried in tables.
  • District and coach views: New roll-up views give school system leaders and instructional coaches visibility across schools, classrooms, and cohorts.
  • Automatic speech recognition (ASR) quality: We're building a formal evaluation harness to establish a quantitative accuracy baseline for our oral reading fluency analysis, with particular attention to multilingual learners.
Product website icon

Product website: https://www.litlab.ai/

Hear from the developer

Instruction Partners' CEO Emily Freitag spoke with Varun Gulati from LitLab to understand what problems they're trying to solve, how the tool works, and what they're still figuring out.

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