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- NewsEdSurge22 Jul 2026
We Must Stop Using AI to ‘Level-Down’ Our Students
This article argues that using AI to simplify texts for students, a practice known as text leveling, reduces the richness of literature and prevents students from engaging in effortful thinking. Instead, teachers should scaffold access to complex texts rather than dumbing them down. The author cautions against the temptation of AI shortcuts in education.
Original abstract
<p>No one would remember the line, “Keep being good and special, Ponyboy. Don’t change.” It just doesn’t hit the same as <a href="https://www.sehinton.com/"><u>S.E. Hinton’s</u></a> classic line, “Stay gold, Ponyboy, stay gold.” </p><p>Many English teachers, myself included, would consider it an atrocity to diminish this canonical line in literary history to such sterile and lifeless simplicity. Students deserve a better reading experience than that. Yet this is just what happens when teachers decide to level a text down to a student’s “instructional level” rather than scaffolding up their access to a complex text. </p><p>In my own district, just two years ago, educators raved about a new AI tool that helps teachers level text and encouraged the department to spend thousands of dollars on it. When I first heard about it, I admit, I was dazzled. The most time-consuming and work-intensive part of teaching for me has always been differentiating for the small percentage of kids who just don’t get it the first, second, or even third time you teach something. </p><p>This program was going to do it all for me, but the caveat was that it would also do all the thinking for the kids. When we keep kids from actually doing work that requires effortful thinking, they don’t learn anything at all. </p><p>While <a href="https://www.thereadingleague.org/compass/policymakers-and-state-education-agencies/"><u>many states</u></a> are passing legislation that focuses on the Science of Reading, moving districts away from programs that promote text leveling, many edtech companies are promoting this practice through their newest AI models. In our world of quick and snappy AI solutions, the leveled text shortcut is all too easy to take, but we should not give in to temptation. </p><h2>The History of Text Leveling</h2><p>The practice of teaching students at their “instructional” level has a long, storied history leading back to the founding colonies. If that sort of history interests you, I
- NewsEdSurge16 Jul 2026
AI Meets ESL, and Teachers Are Intrigued
This news article discusses how artificial intelligence is being used to support bilingual and ESL learners by adapting materials, providing scaffolding in home languages, and enabling chatbots for student-parent dialogues. Teachers and researchers express optimism about AI's potential to close gaps in multilingual education, though they emphasize careful implementation.
Original abstract
<p>As he instructs bilingual learners in the humanities and social studies, Daniel Gomez sees big promise in artificial intelligence.</p><p>“Teachers in general cannot be knowledgeable about every single subject,” and certainly not in multiple languages, says Gomez, who teaches at the bilingual K-12 Ideal School in Tampa, Florida. AI can help close the gaps. “It will be able to pick up any topic students are interested in and handle the topic reasonably well.”</p><p>He isn’t alone in this assessment. Harvard researchers, for example, are actively exploring how AI-powered chatbots can support bilingual student learning and parent–child dialogues.</p><p>“There are lots of opportunities to use AI to adapt learning materials into different languages, which can expand access for bilingual learners,” says<a href="https://www.gse.harvard.edu/directory/faculty/ying-xu"> <u>Ying Xu</u></a>, an assistant professor of education at Harvard and one of the researchers involved in the work.</p><p>“Some projects are also exploring how AI can provide additional scaffolding to help children comprehend content in their home language,” she adds. “For example, a multilingual language model might keep the core learning content in English while offering explanations, prompts or supports in the child’s home language.”</p><p>Experts see great potential for AI to elevate bilingual education — if it’s done right.</p><h2><strong>Chatbots as Educational Aids</strong></h2><p>“Bilingual learners are obviously learning the language and culture of the classroom and the environment that they’re in,” says<a href="https://www.linkedin.com/in/krystlesalas/"> <u>Krystle Salas</u></a>, assistant director of special populations at Second Mile, which operates alternative education charter schools for at-risk students in several states.</p><p>“They don’t necessarily have access to the instructional materials or even just the conversations going on in the classroom — and certainly not at the level of their p
- NewsEdSurge14 Jul 2026
We Talk About Whole Children. What About Whole Educators?
This article argues for supporting educators as whole people, not just employees, using the story of a teacher who thrived when her personal and professional needs were met. It emphasizes that teachers are the engineers of learning experiences, designing and scaffolding every moment for children. The author calls for a shift in the field to truly see and support educators.
Original abstract
<p>In 2010, a young woman walked into my office at the childcare center I directed in Arizona. She was nervous. She didn’t have experience in early childhood education. She just needed a job.</p><p>In most centers, that’s where the story would end. Instead, I offered her a working interview — two observational hours in an infant classroom. When she came back into my office, she was beaming. “I love this,” she told me. “Just give me a chance. I’ll learn.”</p><p>Her name is Lindsay. Fifteen years later, she’s still teaching.</p><h4>Seeing the Educator</h4><p>Lindsay’s story isn’t just about passion or perseverance; it’s about support. We made a deliberate choice — over and over again — to see her as a whole person first and an employee second. We figured out scheduling so she could get her Child Development Associate (CDA) credential. We found coverage when she needed practicum hours elsewhere. I wasn’t there for every step of Lindsay’s journey that followed, but she and I have stayed in touch throughout all these years. She earned her associate’s degree and then her bachelor’s degree; she grew from part-time infant teacher to lead teacher to program coordinator.</p><p>Lindsay’s story shows us what happens when an educator is truly seen — something our field has yet to get right. What gets overlooked most in conversations about the quality of early childhood education is that educators are engineers. Every learning opportunity a young child experiences is designed, built and brought to life by a teacher. There is no curriculum three-year-olds activate for themselves. Every moment of discovery, every language-rich exchange and every carefully scaffolded small group experience is built by someone. A teacher looks at a group of children, weighs their individual needs, considers the family’s hopes, aligns these needs and hopes to learning objectives and makes a decision about what to put in front of those kids at that moment. And they do this all day long.</p><p>These edu
- PaperComputers & Education1 Jul 2026
AI-Generated Podcasts and Learning Outcomes: The Role of Behavioral Engagement and Scaffolding
Erfan Rezvani
Investigates the impact of AI-generated podcasts on learning outcomes, focusing on the roles of behavioral engagement and scaffolding.
- PaperJournal of Learning Analytics15 Mar 2026
A GAI-Based Chatbot for Scaffolding Self-Regulated Learning
Isabel Hilliger, Mar Pérez-Sanagustín, Carlos González, Esteban Villalobos et al.
Generative AI chatbots are increasingly used in education but risk fostering overreliance. This study presents the design and iterative development of a GAI-based chatbot to scaffold self-regulated learning, involving 276 students across 10 courses. Findings highlight the need for explicit guidance, dialogic competence, and educator involvement to ensure AI enhances deep learning and independent thought.
Original abstract
Generative AI (GAI) is increasingly integrated into education, particularly through chatbots that support students without direct human intervention. While these tools show promise as personalized learning companions, concerns persist about their potential to foster overreliance, limit creativity, and hinder the development of critical thinking. These risks highlight the need to strengthen students’ metacognitive skills and promote structured self-reflection. This paper presents the design and iterative development of a GAI-based chatbot aimed at scaffolding self-regulated learning. Through three design cycles involving 276 students in 10 courses, the chatbot evolved from a static assistant into a dynamic, course-integrated tool capable of supporting personalized, Socratic-style dialogue. Thematic analysis of diverse qualitative data sources revealed that students seek scaffolded support, such as human tutoring, and require explicit guidance to engage meaningfully with AI. Findings emphasize the importance of dialogic competence, personalization, and educator involvement in shaping effective AI-mediated reflection. This study underscores the need for pedagogically grounded AI tools that position chatbots as collaborative agents, complementing rather than replacing the roles of teachers and learners. It advocates for reflective teaching practices that clearly define the responsibilities of students, educators, and AI systems to ensure that GAI enhances deep learning and independent thought.
- PaperLanguage Learning & Technology1 Jan 2026
Scaffolding early-stage simultaneous interpreting learning using computerized dynamic assessment
Zhaokun Jiang
Computerized Dynamic Assessment (CDA), which provides adaptive scaffolding during tasks, was applied to early-stage simultaneous interpreting learning. In a pretest-intervention-posttest study with 35 novice interpreters, the CDA group showed greater gains in syntactic complexity and fluency and lower interpreting anxiety compared to a non-CDA group, while accuracy gains were similar. These findings extend CDA benefits from receptive skills to real-time oral production under cognitive and affective demands.
Original abstract
Computerized Dynamic Assessment (CDA) integrates assessment and instruction by algorithmically delivering immediate, adaptive scaffolding to uncover and cultivate learners’ developmental potential. Although CDA has shown promise for receptive language skills (e.g., reading and listening), its effectiveness in simultaneous interpreting (SI) remains underexplored. As a high-stakes bilingual task, SI requires the concurrent processing of source-language input and target-language output, rendering it both cognitively taxing and anxiety-inducing. To address this gap, the present study employed a pretest–intervention–posttest design with 35 novice SI learners from two intact classes, with one class serving as the CDA group (n = 18) and the other as the non-CDA group (n = 17). The CDA group received adaptive, graduated scaffolding targeting five types of complex syntactic structures, whereas the non-CDA group received static, non-graduated support. Interpreting performance was assessed using seven complexity, accuracy, and fluency (CAF) indices, and interpreting-specific anxiety was measured with a Likert-scale questionnaire. Results indicated that the CDA group achieved significantly greater gains in syntactic complexity and fluency and reported lower interpreting anxiety; by contrast, improvements in accuracy were comparable between groups. Overall, these findings provide empirical evidence that the benefits of CDA extend beyond receptive skills to facilitate integrative, real-time oral production under combined cognitive and affective pressure.