Roughly two-thirds of U.S. states have enacted legislation restricting student phone access during the school day, and more than half of countries worldwide have followed suit (D’Addio, 2025; Prothero et al., 2026). Nearly 5,000 schools deployed them by 2026 (Allcott et al., 2026). The pace has outstripped the evidence.
Artificial intelligence does not have to be a black box. In a recent CIDDL webinar, Drs. James Basham, Trey Vasquez from the University of Kansas, and Matthew Marino from the University of Central Florida walked educators through one of the most practical applications of large language models available today: building a custom generative pre-trained transformer, or GPT, trained on information you already trust.
Roughly two-thirds of U.S. states have enacted legislation restricting student phone access during the school day, and more than half of countries worldwide have followed suit (D’Addio, 2025; Prothero et al., 2026). Nearly 5,000 schools deployed them by 2026 (Allcott et al., 2026). The pace has outstripped the evidence.
During this CIDDL Office Hour session, Cheryl Lemke, President and CEO of The Metiri Group, shared early findings from CIDDL’s 2026 Needs Assessment, offering a timely look at how higher education faculty and preparation programs are thinking about educational technology, artificial intelligence, and assistive technology. As CIDDL begins a new five-year cycle, the needs assessment provides an important snapshot of the field’s current priorities and emerging concerns. A central message from the session was clear: faculty are no longer simply asking which tools to use, but are asking deeper questions about pedagogy, student learning, accessibility, ethics, and how to prepare future educators for increasingly AI-rich K–12 environments.
This brief explores how artificial intelligence can support writing instruction through prompt engineering, feedback systems, and increased access to supports. It highlights how AI can reduce barriers to writing while maintaining student ownership and independence. The brief also provides practical recommendations for integrating AI into teacher preparation programs.
Smartphones, tablets, and wearable devices have become natural parts of everyday life. People use mobile devices to navigate, communicate, manage schedules, access information, and monitor health and daily routines (we don’t endorse products or companies; these are simply examples). In special education, however, mobile devices can be more than convenient tools; they can serve as assistive technology that supports students with disabilities in accessing opportunities in academics, transitions, employment, independent living, and community participation.
Finals assess more than content knowledge. They require students to plan, initiate, and persist under pressure. Faculty who use AI to provide structure and support can reduce barriers while maintaining rigor.
One of the most common inquiries CIDDL receives is about Artificial Intelligence and assessment. The integration of AI throughout the education system is affecting nearly everything educators do daily, from small tasks such as emailing students to planning instruction. But one of the largest concerns educators often have about AI is how to design effective assessments. Well, just like nearly everything else with this powerful technology, the key is to understand the basics and then design to support the most effective human interaction.
Attending the 2026 American Educational Research Association (AERA) Annual Meeting in Los Angeles, I was struck by how much the conversation around AI in education has evolved. Across sessions, scholars from diverse disciplines engaged in rich discussions about integrating AI into teaching and learning. Compared to last year, the tone felt notably more mature. Rather than questioning whether AI should be used in education at all, there was a growing recognition that AI is already embedded in daily life and learning environments. The central question is no longer whether to use AI, but how to use it well.
Picture a student who can explain biodiversity in a class discussion but cannot complete a three-step sample collection protocol without losing track of the sequence. Or a student whose curiosity about microorganisms is genuine right up until the moment arrives to document observations in writing. In both cases, the barrier is a lack of understanding. It is the weight of managing multiple demands at once: sequencing steps, monitoring progress, recording data, and switching attention between tools.
This is what executive function breakdown looks like during science instruction, and it is more common than most lesson plans account for.
This is what executive function breakdown looks like during science instruction, and it is more common than most lesson plans account for.