Curriculum and educational technology providers are rapidly embedding artificial intelligence into instructional materials with little to no evidence that these add-ons actually improve student learning. According to a new brief released by EdReports, a prominent nonprofit that reviews pre-K-12 instructional materials, only a single vendor out of the 10 examined in their study included third-party evidence demonstrating that their embedded AI features genuinely improved educational outcomes.
The findings illuminate a growing concern across the educational landscape: as software companies race to capitalize on emerging technological capabilities, behind-the-scenes AI updates are making it increasingly difficult for schools to ensure that the digital products they purchase are truly effective.
"I knew that there wasn’t going to be much evidence, but I was surprised at how little there was," says Courtney Allison, chief academic officer for EdReports. She notes that the report underscores an escalating challenge for school administrators who are tasked with spending public funds responsibly on tools that yield measurable academic results.
This sentiment is echoed by Brittany Miller, executive director of the Center for Outcomes Based Contracting, an organization that helps school districts link edtech procurement directly to academic performance. "This has been a problem for a while with digital tools, and it’s only accelerated with AI," Miller explains. "Tools are growing quickly, and the market is getting crowded and confusing."
Meanwhile, industry advocates argue that the pace of technological development has simply outstripped the traditional academic research cycle. Sara Kloek, vice president of education and youth policy with the Software and Information Industry Association (SIIA), points out that rigorous empirical research "just may not be there yet" for many of the vendors rushing to incorporate artificial intelligence into educator-facing and student-facing products.
The Study and Impact
For its qualitative study, EdReports selected 10 distinct K-12 curriculum and edtech providers to examine closely. The sample was designed to reflect a cross-section of the market: four were among the top 10 publishers of purchased instructional materials in the United States, three were mid-sized curriculum providers, and three were digital-first edtech companies. EdReports researchers reviewed each company’s AI-enabled products, key features, and marketing claims about their use. To maintain objectivity and candor, researchers notified the companies about the review process while keeping their specific identities anonymous.
The consequences of this trend are tangible inside modern classrooms, where the boundaries between traditional course materials and AI-enhanced features have grown increasingly blurred. Jean-Claude Brizard, president and CEO of Digital Promise—a global nonprofit working to expand equitable education opportunities—observes that this opacity threatens traditional quality assurance measures. The worst-case scenario, he warns, is that "quality assurance at adoption no longer guarantees quality at instruction."
Even identifying whether instructional materials actually contain artificial intelligence can present a significant hurdle for educators and administrators, since new capabilities are frequently introduced via quiet software updates without explicit notification from the vendor. "Something as mundane as understanding where that communication was from the vendor that had the new product updates" becomes crucial, says Lora Kaiser, executive director of the Center for Education Market Dynamics, a nonprofit market intelligence organization focused on improving outcomes for underserved students through the adoption of high-quality instructional materials.
Kaiser argues that vendors should give schools a meaningful choice regarding whether to accept new AI updates as they are rolled out. "Is it a feature I can opt in to?" she asks, pointing to the practical realities faced by schools. "For our teachers and our students, anytime that little AI enhancement pops up, that’s an instructional decision. We should know, as a fact, that it’s grounded in evidence, and understand how it impacts instructional integrity for students."
The "Evidence" Gap
The EdReports brief highlights another systemic challenge: while existing evaluation frameworks frequently analyze AI integrations for basic data privacy and physical safety, there is a distinct lack of consistent methodologies to determine whether artificial intelligence actually strengthens learning or preserves curriculum coherence. This absence of standardized evaluative tools creates a major roadblock to establishing genuine educational efficacy.
This problem is further compounded by a lack of fundamental agreement on what the word "evidence" actually means within the educational technology sector, according to Katie Boody Adorno, founder and CEO of Leanlab Education, a nonprofit that co-designs research initiatives between edtech companies and school districts.
"I think the biggest takeaway from the report is that we need to move away from just checking a box around safety and evaluation, and more toward quality: What are the arbiters of quality, and how are we measuring and communicating that?" Adorno asks.
For practitioners on the ground, these abstract concerns manifest as everyday administrative frustrations. Matt Moyer, principal of Rupert Elementary School in Pottstown, Pennsylvania, notes that his school utilizes several educational technology tools that have artificial intelligence embedded within them.
"My take on tools like that is there’s some good in them, but I’m not yet convinced they lead to results outside the usage of that tool itself, which I think is what the report was getting at," Moyer says.
The surprise factor is also remarkably common. "What’s been interesting is realizing, over the last couple of years, that some tools we were already using had AI built in that we simply hadn’t been told about," Moyer continues. When product pitches from edtech vendors do explicitly mention artificial intelligence, he adds, the conversations are usually superficial, built around "what’s best about their AI compared to someone else’s."
While educators naturally want concrete evidence that course materials actually work in practice, Miller of the Center for Outcomes Based Contracting points out another crucial missing piece of the puzzle: the reality of how those tools are actually implemented in a classroom setting.
"The study doesn’t really account for what a student actually does when they engage with the materials," she explains. "I think it’s a matter of sending the right signals from the buyer side to the market: You actually have to help us implement this more effectively, or we’ll see the exact same pattern we’ve seen over the last 20 years—a shiny new object that doesn’t get implemented well, which is why we can’t build evidence."
How Vendors and Schools Can Respond
Addressing the current state of the market will require concerted action from both the companies developing these technologies and the school districts purchasing them. Kloek advises edtech vendors to update their software development practices to align with the latest cybersecurity standards and ethical guidelines. Specifically, she recommends the SIIA’s seven Principles for AI in Education, which serve as a foundational framework designed to guide the edtech industry’s implementation of artificial intelligence.
These principles suggest that AI technology should prioritize core instructional needs, support educational equality, ensure rigorous student data protection, and maintain operational transparency. Furthermore, the framework suggests that edtech companies building AI tools should actively engage with educational institutions regarding new developments, adopt ethical best practices for accountability, and work deliberately to improve AI literacy among both students and educators.
Kloek notes that the SIIA is currently looking to update these principles to include additional transparency documentation for edtech providers, offering clear guidance on how to communicate the purpose and functions of their products to school districts and parents.
"Ultimately, though, the district decides how these tools are used and can decide whether that’s appropriate and aligns with the district’s plans for AI or for technology generally," Kloek says.
To bridge the credibility gap, Adorno believes that edtech vendors need to offer much more specific, verifiable data to back up their marketing claims. "You see a lot of companies saying [their products are] ‘based on cutting-edge research,’ meaning they built the tool around a loose interpretation of some foundational research, often from a university setting," she observes.
EdReports chief academic officer Courtney Allison suggests that local school districts and educational leaders must start demanding significantly more transparency and rigor from their commercial vendors.
"Buyers need to ask the question, ‘What does this badge or this membership or this certification actually demonstrate?’" Allison advises. "They also must start the conversation by asking themselves, ‘What’s the instructional vision for the students?’"
Principal Moyer shares the perspective that promotional evidence provided by edtech vendors frequently tends to raise more questions than it answers. He counsels school and district leaders to probe deeply, asking vendors about the sample size of the research conducted on their product, the qualifications of the researchers, the specific parameters of the study, and other pointed inquiries.
"Educational leaders should look at these claims and ask what they actually translate to, because I’ve seen plenty of cases where a number meant something different once you dug into it," he cautions.
For districts seeking vetted options, Brizard of Digital Promise suggests searching online directories such as the EdTech Index or LearnPlatform to identify providers that have been evaluated for data privacy standards, technical usability, and genuine educational value.
"Current quality frameworks were built for fixed materials, like curricula," Brizard explains. "As edtech and AI integration accelerates, we need a more dynamic system of measuring quality and efficacy."