Generative
Artificial Intelligence (AI) has significant potential to improve the
efficacy of Science of Reading approaches. Science of Reading is not a single program or
product but is constantly evolving based on scientific consensus and
peer-reviewed research (Moats, 2020). While embracing the importance of phonemic and phonetic awareness skill development, Science of Reading also incorporates Scarborough’s Reading Rope,
which highlights the need for both word recognition skills and language
comprehension strategies or proficiencies. This ensures that students don’t
just read words but are able to understand and engage with texts.
AI-powered platforms can assess
students’ reading abilities in real time and adjust subsequent instruction
accordingly. Adaptive learning tools can provide differentiated instruction
based on a student’s phonemic awareness, decoding skills, and fluency. AI can
track patterns in errors and provide targeted interventions.
The need for effective Teacher
Preparation is more crucial than ever. AI can support teacher
candidates by simulating real-life classroom scenarios whereby they practice Science
of Reading-aligned strategies. AI-driven
coaching tools can provide immediate feedback on lesson delivery, phonics
instruction, or error correction. AI can
analyze large datasets to identify which instructional strategies are most
effective in different contexts.
Data-driven formative feedback must
be valid to provide reliable insights for instruction. AI can analyze reading assessments and
progress monitoring data to identify trends and predict which students may need
early intervention. AI-driven analytics
can help schools and teacher preparation programs refine their Science of
Reading-based curricula.
AI-Assisted lesson
plans and reading interventions can support struggling readers through
speech recognition and natural language processing tools that guide
pronunciation, fluency, and comprehension. Diagnostically, intelligent tutoring
systems can provide scaffolded tiered support to students based on their diverse
needs. AI can assist students with reading disabilities by providing customized
text-to-speech, visual modifications, or alternative instructional approaches.
Generative AI can help achieve the
goal of being effective with students while being efficient with time. AI can generate instant feedback on student
reading fluency, pronunciation, and comprehension. As a result, AI-powered
assessment tools can reduce teacher workload while maintaining high-quality,
data-driven instruction.
Crucially, Generative AI should be
used as a supplement rather than a replacement for an expert teacher. The professional educator will ensure concerns
around data privacy and bias in AI modeling are ethically addressed.
Therefore, despite the potential for generalization indicated by previous
studies, the effectiveness of Generative AI being aligned with evidence-based Science
of Reading practices depends on the quality of training and reliable use of
data.
A comprehensive meta-analysis
evaluated the effectiveness of AI interventions in language learning,
considering various contextual, instructional, and social-emotional factors (Wu,
2024) and concluded AI interventions significantly improved language
learning outcomes, particularly in listening and speaking skills.
Medium-duration AI interventions ranging from 6 weeks to 6 months in online and
blended learning environments were most effective. Additionally, AI tools
positively influenced motivation and reduced language anxiety among learners. Therefore,
personalized and adaptive AI tools, especially those providing diverse feedback
sources, exhibits the potential to optimize language learning outcomes.
A mixed-methods study of university students
examined the effects of AI-mediated language instruction on English learning
achievement, motivation, and self-regulated learning among English as a Foreign
Language (EFL) learners (Wei, 2023). The experimental group that received
AI-mediated instruction demonstrated significantly higher outcomes in grammar,
vocabulary, reading comprehension, and writing skills compared to the control
group. Furthermore, the experimental group exhibited increased motivation and utilized
more self-regulated learning strategies.
AI-driven learning environments were
found to effectively promote reading comprehension and foster positive learning
behaviors in a study by Shafiee
Rad (2025) whereby the impact of AI-based platforms on reading
comprehension, engagement, and self-regulated learning among students was
investigated. Compared to those students receiving traditional instruction, students
using the AI-enhanced platform showed significant improvements in reading
comprehension and reported higher levels of engagement and self-regulation. To
what extent can AI-driven learning environments effectively promote reading
comprehension and foster positive learning behaviors will require further
research.
As reported by Hollingsworth
(2024) AI-powered assistive technologies are being integrated into
educational settings to support students with disabilities, including those
with reading challenges such as dyslexia. By offering features like speech
recognition and word prediction, these AI tools aid students in reading and
writing tasks and promoting inclusivity in literacy education. While AI
provides valuable support, it's essential to balance its use to ensure it
complements traditional learning methods without fostering overreliance. Although
AI is a game changer for students with disabilities, staff and faculty at educational
institutions are still learning how to
utilize and implement it.
These studies and potential applications
illustrate the growing role of AI in enhancing reading instruction and aligning
with the principles of the Science of Reading. They underscore AI's potential
to personalize learning, boost engagement, and support diverse learner needs. However,
they also highlight the importance of thoughtful implementation and ongoing
evaluation to address challenges and maximize benefits.
Several AI-powered tools align with
the Science of Reading principles, focusing on phonemic awareness, phonics,
fluency, vocabulary, and comprehension. These tools provide personalized
learning, adaptive instruction, and real-time feedback to enhance literacy
instruction. Based on categorically emphasis, let’s discuss established
programs and those gaining traction to better understand how Generative AI can
improve the efficacy of Science of Reading approaches.
Amplify Reading and Lexia Core5
Reading provide AI tools for phonemic
awareness and phonics. Amplify Reading uses
AI to personalize phonics instruction and scaffold phonemic awareness through adaptive
learning paths, real-time student data, and Science of Reading -aligned
decodable texts. Its programming is most suitable for K-5 students,
particularly struggling readers. Lexia
Core5 Reading provides structured literacy instruction, targeting phonemic
awareness, phonics, and fluency through AI-driven progress monitoring, explicit
instruction, and scaffolded support. Its programming addresses the needs of Pre-K-5
students needing systematic phonics instruction.
ReadWorks and Curipod provide AI tools for fluency and reading comprehension. ReadWorks uses AI to enhance
comprehension through text scaffolding and question generation. The program provides
differentiated reading assignments, AI-powered text simplification, and
comprehension quizzes. It is recommended for students in Grades 1-12,
particularly for improving comprehension skills. Curipod AI generates
interactive Science of Reading-aligned lesson plans and assessments. Its AI-driven
lesson creation, real-time comprehension checks, and engagement analytics can
help teachers seeking to design structured reading lessons.
SoapBox Labs and Read AI by
Microsoft provide tools for AI-powered reading intervention. SoapBox Labs uses AI for speech
recognition in literacy, through voice AI to analyze reading fluency and
pronunciation errors. Its AI-driven speech-to-text analysis, real-time
feedback, and Science of Reading-aligned reading fluency support early readers
and struggling readers needing pronunciation support. Read AI by Microsoft tracks reading
progress, provides real-time feedback, and assesses fluency. Its AI-generated
reading reports, automatic error detection, and personalized interventions are
a resource for teachers needing automated fluency assessments.
Quillionz and ChatGPT provide AI tools
for vocabulary and language development.
Quillionz uses AI to generate comprehension
questions and quizzes based on Science of Reading-aligned texts. Its AI-powered
question generation, vocabulary reinforcement, and interactive activities
supports teachers seeking to design vocabulary and comprehension assessments. ChatGPT can be adjusted to generate
decodable texts, phonics exercises, and reading interventions based on Science of Reading
principles. The result is the potential for AI-driven lesson planning,
real-time student engagement tools, and scaffolded learning activities that
support educators seeking to create custom Science of Reading-aligned content.
Regardless
of the instructional intervention, it is crucial to
recognize efficacy does not entail teaching alone. A highly qualified teacher understands the
ongoing relationship between the curriculum, his or her instruction, and
ongoing assessment of learning. So, again,
AI should supplement and never replace
structured Science of Reading instruction.
The effective educator will regularly and reliably assess student
progress and adapt the utilization of AI accordingly. Lastly, we all must be mindful and vigilant of
student data security in AI-powered platforms.
To cite:
Anderson, C.J. (March 31, 2025) Merging relevant topics: Improving
the efficacy of science of reading approaches with generative artificial
intelligence. [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/
References:
Moats, L. C. (2020). Speech to print: Language essentials for teachers (3rd ed.).Brookes Publishing.
Shafiee Rad, H. (2025). Reinforcing L2 reading comprehension through artificial intelligence intervention: refining engagement to foster self-regulated learning. Smart Learning. Environments. 12, 23. https://doi.org/10.1186/s40561-025-00377-2
Wei L. (2023) Artificial intelligence in language
instruction: impact on English learning achievement, L2 motivation, and
self-regulated learning. Front Psychology. 14:1261955. doi:
10.3389/fpsyg.2023.1261955. PMID: 38023040; PMCID: PMC10658009.
Wu, X.Y (2024) Artificial intelligence in L2 learning: A meta-analysis of
contextual, instructional, and social-emotional moderators. System, Volume
126, 103498, ISSN 0346-251X, https://doi.org/10.1016/j.system.2024.103498
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