Recall
that last May 2024, we noted that a Professional
Learning Community (PLC) invites learning by doing and that this process is optimized through intentional,
caring, optimistic, respectful, and trustworthy (ICORT) mindsets (Purkey
& Novak, 2016; Anderson, 2021), which fosters
a positive and supportive learning environment.
It was suggested that a school having a PLC focusing on generative
artificial intelligence (AI) policy
development and best-practice curriculum integration can identify where
additional support or training was needed.
This month let’s model the work of a PLC using Open AI to concretely
exhibit how to optimize differentiated Multi-Tiered Systems of Support (MTSS) into
instructional groups that address diverse learning needs. Yes, elementary teachers can
use generative artificial intelligence to plan implementation of
differentiation to effectively address the diverse reading needs of their
learners (OpenAI, personal communication, January 31, 2025). Minimally, AI can
help the Elementary teacher by:
• Assessing and
Monitoring Student Progress
• Creating
Personalized Reading Interventions
• Helping to
Differentiate Instructional Planning
• Automating
Administrative Tasks
• Supporting
Teacher Collaboration and Professional Development
How can generative artificial intelligence help assess and monitor
student progress? AI can analyze student reading levels through online
assessments, oral reading fluency tools, or comprehension checks to determine
which MTSS tier is appropriate. Then AI can continuously monitor student growth
and flag those needing interventions. Adaptive learning platforms such as Lexia
or i-Ready can provide real-time feedback to teachers.
Can generative artificial intelligence help create personalized
reading interventions? AI-driven reading platforms adjust difficulty based on
individual student responses, providing tailored instruction for Tier 1 (core
instruction), Tier 2 (targeted intervention), and Tier 3 (intensive
intervention). So, yes. Tools like ChatGPT can generate individualized reading
exercises, comprehension questions, and phonics activities based on student
data.
How can generative artificial intelligence help teachers
differentiate instructional planning? AI can suggest differentiated lesson
plans based on student data, incorporating scaffolding strategies, leveled
texts, and multisensory activities. Ideally, this will be further shown below.
Crucially, AI tools like ChatGPT can rewrite passages at different reading
levels, ensuring accessibility for all learners.
How might AI help teachers by automating administrative tasks?
Well, AI can suggest fluid grouping based on ongoing assessment data, ensuring
students receive appropriate interventions. This helps with data-driven
decision making through generated reports for teachers, highlighting trends in
student progress and recommending instructional strategies.
Lastly, let’s consider how AI can support and further enhance
teacher collaboration and professional development. Teachers can use AI to find
research-based strategies, lesson ideas, and evidence-based interventions
aligned with MTSS, RTI, or whatever your school calls its tiered intervention
approach. AI-powered coaching tools can recommend instructional strategies and
provide just-in-time learning opportunities for teachers.
In this regard, let’s now review a sample of AI-powered
interventions for groups of elementary students that are often placed in a
single, fourth grade public school classroom. Most elementary teachers should
recognize the following sample of reading profiles. In today’s inclusive environments it is usual
to find students reading up to two-levels below grade, students that are
English language learners, and students at grade level that struggle with
effectively implementing essential literacy strategies.
Early Grade 4,
Sample Group: Tier 3 Profile:
Reading Challenges: Difficulty decoding multisyllabic words,
struggles with sight word recognition, slow reading fluency. 2nd grade reading
level.
Assessment Data:
• Oral Reading
Fluency: 35 WPM (below benchmark of 50 WPM)
• Phonics Screener:
Difficulty blending long vowel sounds and consonant blends
• Comprehension:
Struggles with understanding when reading independently
AI-Powered Intervention Plan
1. Targeted Phonics Instruction (15 min daily – Small Group or
1:1)
• AI Tools:
Lexia Core5 or Reading Horizons: Adaptive phonics lessons
personalized to Students’ specific skill gaps.
ChatGPT-based phonics practice: AI generates custom decodable word
lists based on phonics patterns students struggles with (e.g., “silent e”
words: hope, tape, bike).
• Activities:
AI-generated phonics word sorts (real vs. nonsense words).
Voice-assisted blending exercises where AI pronounces words, and students
decipher them.
2. Fluency Development (15 min – Paired & Independent
Practice)
• AI Tools:
ReadTheory or Raz-Kids: AI-adaptive passages at her level,
tracking her WPM progress.
Speech-to-Text AI (e.g., FluencyTutor by Texthelp):
Analyzes students’ oral reading for accuracy, rate, and expression.
• Activities:
AI-generated repeated reading passages: Students read a passage
multiple times, AI tracks improvement.
Echo Reading with AI tutor: AI reads aloud, Students repeat for
modeling fluency.
3. Sight Word Recognition (10 min – Gamified Practice)
• AI Tools:
Quizlet AI-powered flashcards with voice recognition for automatic
correction.
AI-generated sentences using high-frequency words in meaningful
contexts.
• Activities:
AI reads sight words aloud; Students type what she hears.
AI gamifies sight word recognition (e.g., matching words to
pictures).
4. Comprehension & Vocabulary (15 min – Digital & Hands-on
Activities)
• AI Tools:
ChatGPT-generated leveled texts: Adjusts reading passages to her
comprehension level.
Kid-friendly AI chatbots (e.g., BookBot) to ask interactive
comprehension questions.
• Activities:
AI provides oral comprehension questions with scaffolding.
AI suggests graphic organizers for students to map story elements.
5. Progress Monitoring & Adjustments
• AI Tools:
AI Dashboard (e.g., i-Ready, Star Reading) to
track fluency, phonics progress, and comprehension growth.
ChatGPT-generated weekly reports summarizing areas of progress and
next steps.
• Teacher Actions:
Review AI data weekly for adjustments in intervention intensity.
Shift focus areas based on real-time performance.
Outcome Goals (6-8 Weeks):
Increase fluency to
50+ WPM.
Master 10+ new
sight words per week.
Improve phonics
decoding of vowel teams and silent "e" words.
Demonstrate
improved comprehension with 80% accuracy in response to reading.
(OpenAI, personal communication, January 31, 2025)
Early Grade 4, Sample Group: Tier 2
Vocabulary & Fluency Intervention for ELL Students
Challenges:
• Limited academic
vocabulary knowledge
• Struggles with
English pronunciation and fluency
• Needs support
understanding idioms and figurative language
Assessment Data:
• WIDA ACCESS
Score: 3.5 (Developing level in reading & speaking)
• Fluency: Reads 50
WPM, below the 3rd-grade benchmark of 80 WPM
AI-Powered Intervention Plan
1. Vocabulary Acquisition (15 min – Interactive Practice)
AI Tools:
• ChatGPT-generated
vocabulary games: AI creates custom fill-in-the-blank exercises using targeted
words.
• Visual dictionary
tools (like Rewordify or LingQ): AI
converts texts into simpler forms with image support.
Activities:
• AI-generated
sentence completion tasks: Student fills in missing words in context.
• Real-world
application: AI suggests real-life conversation starters using new words.
2. Fluency Development (15 min – Repeated Reading & AI
Feedback)
AI Tools:
• Speech-to-Text AI
(like Fluency
Tutor or Google Read Aloud): AI evaluates students’ reading pace,
pronunciation, and expression.
• AI-read-aloud
apps (like NaturalReader): Provides native-speaker modeling.
Activities:
• Choral reading
with AI: AI reads first, Student echoes for fluency practice.
• AI-generated text
chunking: AI breaks sentences into meaningful parts to improve pacing.
3. Comprehension & Conversation (20 min – Discussion &
Response)
AI Tools:
• BookBot or
ChatGPT conversational prompts: AI asks inferential and open-ended questions.
• AI-generated
comic strips: Helps Student visualize and discuss language concepts.
Activities:
• AI-simulated
conversation: AI provides dialogue practice with automatic corrections.
• Story retelling
with AI feedback: Students retell a story, and AI prompts for richer language
use.
4. Progress Monitoring & Adjustments
AI Tools:
• AI-generated
weekly pronunciation feedback using speech recognition tools.
• AI progress
tracker (like WIDA
Can-Do Descriptors) to evaluate fluency growth.
Teacher Actions:
• Adjust vocabulary
difficulty based on AI insights.
• Increase speaking
tasks as fluency improves.
Outcome Goals (6-8 Weeks):
Increase oral
reading fluency to 80+ WPM.
Expand Tier 2
vocabulary knowledge by 15+ words per week.
Use complete
sentences and basic idioms in conversations.
(OpenAI, personal communication, January 31, 2025)
Early Grade 4, Sample Group: Tier 2
Reading Comprehension Intervention
Reading Challenges:
• Struggles with
identifying main ideas and details
• Difficulty making
inferences from texts
• Reads fluently
but lacks comprehension
Assessment Data:
• Reading Lexile:
650L (below grade level expectation of 770L)
• Low scores on
inference-based and summarization questions
AI-Powered Intervention Plan
1. Explicit Comprehension Strategy Instruction (20 min – Small
Group or 1:1)
AI Tools:
• ChatGPT-generated
scaffolding prompts: AI provides sentence starters for summarizing or
inferencing.
• ReadTheory or
Newsela: AI-adaptive reading passages with comprehension quizzes at Students’
level.
Activities:
• AI-generated
"Think Aloud" modeling: AI reads passages aloud, pausing to verbalize
thought processes.
• Summarization
scaffolding: AI provides chunked text with guiding questions like “What was the
problem?”
2. Vocabulary Development for Deep Understanding (15 min –
Interactive Activities)
AI Tools:
• ChatGPT synonym
expansion: AI suggests words with different levels of complexity to deepen
understanding.
Activities:
• AI-generated
context clues exercises: Students guess word meanings based on AI-provided
example sentences.
• Semantic mapping:
AI generates word association maps for deep learning.
3. Guided Close Reading Practice (20 min – Text Annotation &
Discussion)
AI Tools:
• CommonLit
or Actively Learn: AI-annotated passages with guiding questions.
• ChatGPT-generated
comprehension questions: Adjusts based on Students’ responses.
Activities:
• AI suggests text
annotation prompts (“Highlight evidence supporting the character’s emotions”).
• AI-driven
Socratic discussions: AI chatbot engages students in text-based discussions.
4. Progress Monitoring & Adjustments
AI Tools:
• AI Dashboard
(i-Ready, Star Reading) to track comprehension growth.
• ChatGPT-generated
weekly reports with skill breakdowns and next steps.
Teacher Actions:
• Review AI
insights on students’ inference and main idea performance.
• Adjust text
complexity every 2 weeks based on growth.
Outcome Goals (6-8 Weeks):
Accurately answer
80% of inference and main idea questions.
Improve reading
Lexile by at least 75L.
Write clear,
concise summaries of grade-level texts.
(OpenAI, personal communication, January 31, 2025)
Ideally,
it has been helpful to discuss the benefits of an AI-focused Professional
Learning Community at your school to be a
place for collaborative teaching and learning. Unquestionably, it also needs
to provide regular evaluation and feedback.
Monitoring and evaluating research projects will ensure compliance with
ethical standards. The PLC can establish channels for educators to provide
anonymous feedback on ethical concerns within the community.
By
reviewing and reflecting upon the sample AI-Powered Intervention Plans above, perhaps
you feel more enthusiastic or empowered to participate in your school’s AI-focused
PLC. An effective educational leader promotes transparency and professional
development. The goal should be to always create a culture of ethical research
practice among educators and learners. You are professionally invited to ensure
that generative
artificial intelligence is used
responsibly and effectively.
To Cite:
Anderson, C.J. (May 31, 2024). Embracing AI to create effective reading groups providing multi-tiered systems of support [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/
References:
Anderson, C. J. (2021). Developing your students' emotional intelligence and
philosophical perspective begins with I-CORT. Journal of Invitational
Theory and Practice, 27, 36-50.
Atlas, S. (2023) ‘ChatGPT for higher education
and professional development: A guide to conversational AI’, College of
Business Faculty Publications [Preprint]. Available at: https://digitalcommons.uri.edu/cba_facpubs/548
DuFour, R., DuFour, R., &
Eaker, R. (2008). Revisiting professional learning communities at
work: New insights for improving schools.
Bloomington, IN: Solution Tree Press.
Dufour, R. (2006).
Learning by doing: A handbook for professional learning communities at work. Bloomington,
IN: Solution Tree Press.
Purkey, W. W., & Novak, J. M. (2015). Fundamentals
of invitational education. (2nd Ed) International
Alliance for Invitational Education. Retrieved from: Fundamental of Invitational
Education | IAIE
U.S. Department of Education, Office of
Educational Technology (2023), Artificial Intelligence and Future of Teaching
and Learning: Insights and Recommendations. Retrieved from: Artificial Intelligence and
the Future of Teaching and Learning (ed.gov)
