AI student simulates real reading errors in spoken sessions
Student error rate shifts with each correction’s quality
Corrections scored on accuracy, clarity, scaffolding, and encouragement
The Literacy Architects, LLC
Washington, D.C
Education Services / Teacher Professional Development
Amazon Bedrock (Nova Lite), Amazon Polly (Neural Text-to-Speech), Amazon Transcribe, Amazon EC2, Terraform, React
The Literacy Architects, an education consulting organization that trains teachers in evidence-based phonics instruction, wanted to give teachers a more realistic way to practice correcting student reading errors than traditional peer role-play. Avahi built a voice-based AI tutor that simulates a student reader, produces developmentally accurate phonetic errors, and adapts in real time to the quality of a teacher’s correction. The application runs on Amazon Transcribe, Amazon Polly, and Amazon Bedrock, giving teachers a live, spoken practice session rather than a scripted transcript. The engagement moved from initial discovery through a working, teacher-tested application deployed in the Literacy Architects’ own AWS environment.
The Literacy Architects is an education consulting organization that partners with teachers, instructional coaches, and school districts to improve literacy instruction outcomes. The organization delivers professional development, structured training programs, and instructional support centered on evidence-based reading practices, including phonics, phonemic awareness, and error-correction strategies, with an emphasis on practical techniques teachers can apply directly in the classroom.
Teachers preparing to deliver structured literacy instruction have traditionally practiced correcting student reading errors through peer role-play, with one adult standing in for a struggling reader. This approach depends on the availability of a practice partner, varies in quality from session to session, and does not reliably reproduce the specific phonetic error patterns real students make.
Without a scalable way to rehearse these interactions, teachers had fewer opportunities to build confidence and instructional fidelity before working with actual students. The Literacy Architects needed a practice environment that could simulate authentic student reading behavior, including developmentally appropriate errors across phoneme isolation, blending, and segmentation, and respond dynamically to how well a teacher corrected each error.
As an education consultancy delivering professional development to schools and districts, The Literacy Architects needed a platform that could support natural speech interaction and realistic AI-generated responses while keeping operating costs low and predictable. AWS’s combination of speech, language, and generative AI services in a single ecosystem, along with a straightforward path to running the application inside the Literacy Architects’ own AWS account, made it a practical fit for a lean, teacher-facing tool.
Avahi’s experience building generative AI engagements on AWS made it a strong partner for turning an instructional concept into a working, voice-driven application within a short timeframe. The Literacy Architects wanted a technical team that could move quickly through discovery, iterate closely with their instructional experts, and build a system tuned to the specific pedagogy of phonemic awareness rather than a generic conversational bot. Avahi’s collaborative, weekly delivery cadence let the Literacy Architects’ team shape the product hands-on through live demos and fast revisions.
Avahi built the Phonics Voice Tutor, a web application that lets a teacher run a live, voice-based practice session with an AI-simulated student. A teacher configures a lesson through a guided, multi-section form, selecting target phonemes, focus skills (isolation, blending, or segmentation), and a starting proficiency score for the simulated student. The system generates a structured lesson plan of warm-up and main exercises, then walks the teacher through each exercise in a conversational session.
During a session, the teacher speaks a prompt, which Amazon Transcribe converts to text through a streaming, two-step review flow so the teacher can confirm or correct the transcript before it is submitted. The simulated student then responds, either correctly or with a phonemic error modeled on real classroom patterns, and Amazon Polly’s neural text-to-speech engine synthesizes the response as spoken audio using phoneme-level tagging for precise pronunciation. If the student errs, the teacher delivers a correction, and Amazon Bedrock evaluates that correction across four dimensions, accuracy, clarity, scaffolding, and encouragement, and returns a coaching tip. The underlying pipeline was built for extensibility: error patterns, evaluation criteria, and individual components can be modified or swapped out as the Literacy Architects’ needs evolve, without reworking the overall architecture.
The simulated student’s error rate adapts continuously: strong corrections lower the likelihood of a future error, while weaker corrections hold or raise it, so session difficulty responds to how well the teacher is coaching. The system also supports a mode that models Spanish-English phonemic interference, letting teachers practice with error patterns specific to English Language Learners.
The application’s infrastructure is fully managed through Terraform as infrastructure as code, following AWS’s Operational Excellence best practices so the environment can be scaled, stopped, or torn down quickly with minimal manual effort. The frontend is a React interface that guides the teacher from lesson setup through the live session, including a scaffold image panel and an on-screen phoneme keyboard so teachers can correct any transcription errors from speech recognition.
No production usage data was available at the time of this case study, so the results below reflect delivery and readiness outcomes rather than measured classroom results. Teachers were able to complete full practice sessions independently, and the Literacy Architects team used the completed application to validate error realism and refine student behavior ahead of a possible next phase. The engagement concluded with a fully deployed, signed-off application running in the client’s own AWS environment, along with the documentation needed for their team to operate and extend it.
The Literacy Architects, LLC
Washington, D.C
Education Services / Teacher Professional Development
Amazon Bedrock (Nova Lite), Amazon Polly (Neural Text-to-Speech), Amazon Transcribe, Amazon EC2, Terraform, React
The Literacy Architects, an education consulting organization that trains teachers in evidence-based phonics instruction, wanted to give teachers a more realistic way to practice correcting student reading errors than traditional peer role-play. Avahi built a voice-based AI tutor that simulates a student reader, produces developmentally accurate phonetic errors, and adapts in real time to the quality of a teacher’s correction. The application runs on Amazon Transcribe, Amazon Polly, and Amazon Bedrock, giving teachers a live, spoken practice session rather than a scripted transcript. The engagement moved from initial discovery through a working, teacher-tested application deployed in the Literacy Architects’ own AWS environment.
The Literacy Architects is an education consulting organization that partners with teachers, instructional coaches, and school districts to improve literacy instruction outcomes. The organization delivers professional development, structured training programs, and instructional support centered on evidence-based reading practices, including phonics, phonemic awareness, and error-correction strategies, with an emphasis on practical techniques teachers can apply directly in the classroom.
Teachers preparing to deliver structured literacy instruction have traditionally practiced correcting student reading errors through peer role-play, with one adult standing in for a struggling reader. This approach depends on the availability of a practice partner, varies in quality from session to session, and does not reliably reproduce the specific phonetic error patterns real students make.
Without a scalable way to rehearse these interactions, teachers had fewer opportunities to build confidence and instructional fidelity before working with actual students. The Literacy Architects needed a practice environment that could simulate authentic student reading behavior, including developmentally appropriate errors across phoneme isolation, blending, and segmentation, and respond dynamically to how well a teacher corrected each error.
As an education consultancy delivering professional development to schools and districts, The Literacy Architects needed a platform that could support natural speech interaction and realistic AI-generated responses while keeping operating costs low and predictable. AWS’s combination of speech, language, and generative AI services in a single ecosystem, along with a straightforward path to running the application inside the Literacy Architects’ own AWS account, made it a practical fit for a lean, teacher-facing tool.
Avahi’s experience building generative AI engagements on AWS made it a strong partner for turning an instructional concept into a working, voice-driven application within a short timeframe. The Literacy Architects wanted a technical team that could move quickly through discovery, iterate closely with their instructional experts, and build a system tuned to the specific pedagogy of phonemic awareness rather than a generic conversational bot. Avahi’s collaborative, weekly delivery cadence let the Literacy Architects’ team shape the product hands-on through live demos and fast revisions.
Avahi built the Phonics Voice Tutor, a web application that lets a teacher run a live, voice-based practice session with an AI-simulated student. A teacher configures a lesson through a guided, multi-section form, selecting target phonemes, focus skills (isolation, blending, or segmentation), and a starting proficiency score for the simulated student. The system generates a structured lesson plan of warm-up and main exercises, then walks the teacher through each exercise in a conversational session.
During a session, the teacher speaks a prompt, which Amazon Transcribe converts to text through a streaming, two-step review flow so the teacher can confirm or correct the transcript before it is submitted. The simulated student then responds, either correctly or with a phonemic error modeled on real classroom patterns, and Amazon Polly’s neural text-to-speech engine synthesizes the response as spoken audio using phoneme-level tagging for precise pronunciation. If the student errs, the teacher delivers a correction, and Amazon Bedrock evaluates that correction across four dimensions, accuracy, clarity, scaffolding, and encouragement, and returns a coaching tip. The underlying pipeline was built for extensibility: error patterns, evaluation criteria, and individual components can be modified or swapped out as the Literacy Architects’ needs evolve, without reworking the overall architecture.
The simulated student’s error rate adapts continuously: strong corrections lower the likelihood of a future error, while weaker corrections hold or raise it, so session difficulty responds to how well the teacher is coaching. The system also supports a mode that models Spanish-English phonemic interference, letting teachers practice with error patterns specific to English Language Learners.
The application’s infrastructure is fully managed through Terraform as infrastructure as code, following AWS’s Operational Excellence best practices so the environment can be scaled, stopped, or torn down quickly with minimal manual effort. The frontend is a React interface that guides the teacher from lesson setup through the live session, including a scaffold image panel and an on-screen phoneme keyboard so teachers can correct any transcription errors from speech recognition.
No production usage data was available at the time of this case study, so the results below reflect delivery and readiness outcomes rather than measured classroom results. Teachers were able to complete full practice sessions independently, and the Literacy Architects team used the completed application to validate error realism and refine student behavior ahead of a possible next phase. The engagement concluded with a fully deployed, signed-off application running in the client’s own AWS environment, along with the documentation needed for their team to operate and extend it.
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