Education
Pragmatic AI in education and its role in mathematics learning and teaching
Figure 1 also indicates the potential for AI (the robot icon) to contribute to the development of the underlying educational goals (SEL development in this example), as well as assisting in mediating both the balancing and reinforcing feedback loops (B1 and R2) associated with emotions. Some of these envisaged points for interaction are autonomous, where the robot icon appears alone, and some are supervised, where the robot and mortarboard coexist. It is important to note that these potential points of AI support are entirely indicative and the examples that are presented here are general suggestions.
Before exploring Fig. 1 in detail, it is important that we emphasise the cyclical nature of this model. Learning does not happen as a one-off event. Rather the objects of education (SEL development in this example) result in a series of changes in other quantities that are then available for use in future cycles. It is also essential to recognise that the system presented in Fig. 1 is incomplete and forms just one sub-system in a much more complex process. That is, there will be other factors in the larger system connected to the nodes of our model that are not represented here (e.g., resilience). For this reason, it is important that students experience both successes in learning and difficulties. Each will cause different emotional responses, and the larger system will respond in different ways potentially developing attributes like perseverance that are not represented here. Any AI implementations that become incorporated into this system need to ensure that they are designed to respond appropriately to both success and difficulty in ways that enhance the underlying educational objectives without disempowering students or their teachers or shortcutting the learning processes.
Working from our conceptual model (Fig. 1), we propose six general areas where AI may be able to provide mechanisms or approaches that support the learning process. From these we have identified specific elements of learning design that may be targeted for research, improvement, or modification. These objects of transformation and their associated design goals are presented in Table 1 along with possible AI application strategies and examples of implementation.
Firstly, supporting teachers to provide mathematical learning activities that are personalised at an appropriate level of complexity to match the cognitive abilities of individual students is essential. The strengths of current approaches to AI lie in their ability to mimic human processes, using vastly larger quantities of data, and with far greater speed that humans can achieve. When an AI is presented with a problem that is similar to ones it has ‘seen’ before, then it is reasonable to expect the algorithm to follow a similar process in generating output. Therefore, the implementation of some form of semi-supervised, or even unsupervised, algorithm to analyse each student’s past and current learning performance and/or mastery data and to suggest learning pathways for them to follow or to dynamically adjust the task demands to an appropriate level would be a potentially effective use for an AI learning support. Such an implementation would appear at ① in Fig. 1 and provide personalised challenges that maintain an optimal balance between task demand and cognitive quality of instruction. These individualised learning experiences will align with each student’s proficiency level, minimising their frustration and enhancing the likelihood of successful learning outcomes. However, personalisation should not be limited to task difficulty alone. AI can help pinpoint specific moments where students struggle, providing feedback that is not only task-focused but also supports positive appraisals of their efforts. For example, AI-driven systems can identify when a student shows signs of frustration or disengagement and offer supportive feedback that highlights their progress and effort rather than solely focusing on task completion. This can help students reinterpret their experiences as opportunities for learning, thereby fostering a more positive self-concept and increasing their motivation. An algorithm such as this would be best suited for general implementation by resource producers, as they would have ready access to the large amounts of data needed to establish the parameters of the algorithm. However, in a semi-supervised arrangement, some system or process would need to be developed to allow the classroom teacher to provide input and fine tune the algorithm in a straightforward and intuitive way.
Extending the idea of performance monitoring to student self-monitoring, AI algorithms can be used to enhance learners’ perceptions of agency and control and might be utilised at ② in Fig. 1. Using a large set of learning performance data a resource producer could develop a predictive model that gives students a selection of recommended next steps for their own personalised learning yet remains within the same area of knowledge that the teacher has assigned. In this conception, the AI algorithm essentially creates a decision tree for building learning pathways but leaves the final step of the process, the decision itself, up to the student. Built into such a predictive system would be the opportunity for learning analytics that could build an understanding of each student’s cognitive strengths and weaknesses and provide nudges to assist the student in achieving their own stated goals. AI-enabled tools might also be used in creative learning environments encourage cooperation and thus cultivating positive social interactions. These tools might include interactive and collaborative platforms where students can explore mathematical concepts independently and engage in cooperative problem-solving activities. AI can also contribute to the development of enhanced feelings of control and growth through individualised achievement goal structures and expectations; approaches that are key in reducing maths anxiety.
AI may also assist teachers in enhancing students’ value induction ③ which is an important facet of learning success. AI search engines have access to an almost endless volume of information that could be used to select contexts or situations that highlight the real-world applications and uses of mathematical problems in a way that is relevant to individual students. Such an approach can contribute to students’ understanding of the intrinsic value of mathematical knowledge15.
Chatbots based on large language models (LLMs) have become particularly effective in recent years. While chatbots are still far from perfect, the ability to sideload an LLM with an appropriate set of background data files, does offer educators the possibility to use these algorithms in a safe and closed environment while ensuring that the chatbot has access to only appropriate additional information and not the entire unfiltered web. Adopting such an approach has the added advantage that the LLM itself can be smaller allowing it to run on prosumer level local hardware and removing the need to run on massive cloud infrastructure. The use of an AI approach like this to assist students in moderating feedback loops, such as the one at ④, has great potential for research and impact16. As noted, there is a reinforcing loop between perceptions of control and maths anxiety (R1 in Fig. 1). In a traditional classroom, this feedback loop might be driven by general reflection on the task and its success criteria analysing what the student got wrong and how to fix this. This type of feedback needs to be carefully supervised to ensure that unhelpful self-talk does not dominate the process. An AI chatbot can help to reframe these reflections to consider how an individual has developed in multiple ways while engaging with the learning activities and reduce the dichotomous focus on success vs. failure.
Furthermore, retraining students’ notions of failure and success becomes feasible through AI interventions that emphasise the iterative nature of learning and the value of the underlying educational goal. Such AI tools placed at ⑤ could promote the use of personal micro-targets for students and shift the focus of the cognitive process away from the learning activity and onto this deeper learning goal, AI-driven interventions can lead to a shift in student mindset as they learn to view setbacks as opportunities for growth and reduce the anxiety associated with performance.
However, while current AI tools are capable of ‘decision making’ using large amounts of mostly structured data, they lack the capability to genuinely understand and adapt to the emotional and cognitive needs of students. The AI models tend to focus more on the mechanics of teaching rather than the emotional well-being of the learner. That is, currently available AI tools are more focussed on the products of learning than on the human process of learning. To truly harness the potential of AI in transforming mathematics education, we need to prioritise different research goals. Researchers should therefore aim to develop new systems and approaches that foster a deeper, more intuitive understanding of mathematical concepts, rather than just improving the efficiency of content delivery.
For example, we know that self-regulation of emotions is essential in order to manage adverse situations such as maths anxiety. Natural language processing algorithms might be developed to identify the fingerprints of both positive and negative emotional meaning in students’ extended textual responses. This might be supplemented with data gathered through multi-modal learning analytics—such as computer vision and audio analysis—to track non-lingual cues such as facial expressions, tonal variation and gestures17. Together such a dataset might be able to be used to provide support and strategies, both virtual and real-life, that can assist students in developing their self-regulation and emotional moderation. Such a technology might have a place at ③ or ⑥ in Fig. 1.
Education
Labour must keep EHCPs in Send system, says education committee chair | Special educational needs
Downing Street should commit to education, health and care plans (EHCPs) to keep the trust of families who have children with special educational needs, the Labour MP who chairs the education select committee has said.
A letter to the Guardian on Monday, signed by dozens of special needs and disability charities and campaigners, warned against government changes to the Send system that would restrict or abolish EHCPs. More than 600,000 children and young people rely on EHCPs for individual support in England.
Helen Hayes, who chairs the cross-party Commons education select committee, said mistrust among many families with Send children was so apparent that ministers should commit to keeping EHCPs.
“I think at this stage that would be the right thing to do,” she told BBC Radio 4’s Today programme. “We have been looking, as the education select committee, at the Send system for the last several months. We have heard extensive evidence from parents, from organisations that represent parents, from professionals and from others who are deeply involved in the system, which is failing so many children and families at the moment.
“One of the consequences of that failure is that parents really have so little trust and confidence in the Send system at the moment. And the government should take that very seriously as it charts a way forward for reform.
“It must be undertaking reform and setting out new proposals in a way that helps to build the trust and confidence of parents and which doesn’t make parents feel even more fearful than they do already about their children’s future.”
She added: “At the moment, we have a system where all of the accountability is loaded on to the statutory part of the process, the EHCP system, and I think it is understandable that many parents would feel very, very fearful when the government won’t confirm absolutely that EHCPs and all of the accountabilities that surround them will remain in place.”
The letter published in the Guardian is evidence of growing public concern, despite reassurances from the education secretary, Bridget Phillipson, that no decisions have yet been taken about the fate of EHCPs.
Labour MPs who spoke to the Guardian are worried ministers are unable to explain key details of the special educational needs shake-up being considered in the schools white paper to be published in October.
Stephen Morgan, a junior education minister, reiterated Phillipson’s refusal to say whether the white paper would include plans to change or abolish EHCPs, telling Sky News he could not “get into the mechanics” of the changes for now.
However, he said change was needed: “We inherited a Send system which was broken. The previous government described it as lose, lose, lose, and I want to make sure that children get the right support where they need it, across the country.”
Hayes reiterated this wider point, saying: “It is absolutely clear to us on the select committee that we have a system which is broken. It is failing families, and the government will be wanting to look at how that system can be made to work better.
“But I think they have to take this issue of the lack of trust and confidence, the fear that parents have, and the impact that it has on the daily lives of families. This is an everyday lived reality if you are battling a system that is failing your child, and the EHCPs provide statutory certainty for some parents. It isn’t a perfect system … but it does provide important statutory protection and accountability.”
Education
The Trump administration pushed out a university president – its latest bid to close the American mind | Robert Reich
Under pressure from the Trump administration, the University of Virginia’s president of nearly seven years, James Ryan, stepped down on Friday, declaring that while he was committed to the university and inclined to fight, he could not in good conscience push back just to save his job.
The Department of Justice demanded that Ryan resign in order to resolve an investigation into whether UVA had sufficiently complied with Donald Trump’s orders banning diversity, equity and inclusion.
UVA dissolved its DEI office in March, though Trump’s lackeys claim the university didn’t go far enough in rooting out DEI.
This is the first time the Trump regime has pushed for the resignation of a university official. It’s unlikely to be the last.
On Monday, the Trump regime said Harvard University had violated federal civil rights law over the treatment of Jewish students on campus.
On Tuesday, the regime released $175m in previously frozen federal funding to the University of Pennsylvania, after the school agreed to bar transgender athletes from women’s teams and delete the swimmer Lia Thomas’s records.
Let’s be clear: DEI, antisemitism, and transgender athletes are not the real reasons for these attacks on higher education. They’re excuses to give the Trump regime power over America’s colleges and universities.
Why do Trump and his lackeys want this power?
They’re following Hungarian president Viktor Orbán’s playbook for creating an “illiberal democracy” – an authoritarian state masquerading as a democracy. The playbook goes like this:
First, take over military and intelligence operations by purging career officers and substituting ones personally loyal to you. Check.
Next, intimidate legislators by warning that if they don’t bend to your wishes, you’ll run loyalists against them. (Make sure they also worry about what your violent supporters could do to them and their families.) Check.
Next, subdue the courts by ignoring or threatening to ignore court rulings you disagree with. Check in process.
Then focus on independent sources of information. Sue media that publish critical stories and block their access to news conferences and interviews. Check.
Then go after the universities.
Crapping on higher education is also good politics, as demonstrated by the congresswoman Elise Stefanik (Harvard 2006) who browbeat the presidents of Harvard, University of Pennsylvania and MIT over their responses to student protests against Israel’s bombardment of Gaza, leading to several of them being fired.
It’s good politics, because many of the 60% of adult Americans who lack college degrees are stuck in lousy jobs. Many resent the college-educated, who lord it over them economically and culturally.
But behind this cultural populism lies a deeper anti-intellectual, anti-Enlightenment ideology closer to fascism than authoritarianism.
JD Vance (Yale Law 2013) has called university professors “the enemy” and suggested using Orbán’s method for ending “leftwing domination” of universities. Vance laid it all out on CBS’s Face the Nation on 19 May 2024:
Universities are controlled by leftwing foundations. They’re not controlled by the American taxpayer and yet the American taxpayer is sending hundreds of billions of dollars to these universities every single year.
I’m not endorsing every single thing that Viktor Orbán has ever done [but] I do think that he’s made some smart decisions there that we could learn from.
His way has to be the model for us: not to eliminate universities, but to give them a choice between survival or taking a much less biased approach to teaching. [The government should be] aggressively reforming institutions … in a way to where they’re much more open to conservative ideas.”
Yet what, exactly, constitutes a “conservative idea?” That dictatorship is preferable to democracy? That white Christian nationalism is better than tolerance and openness? That social Darwinism is superior to human decency?
The claim that higher education must be more open to such “conservative ideas” is dangerous drivel.
So what’s the real, underlying reason for the Trump regime’s attack on education?
Not incidentally, that attack extends to grade school. Trump’s education department announced on Tuesday it’s withholding $6.8bn in funding for schools, and Trump has promised to dismantle the department.
Why? Because the greatest obstacle to dictatorship is an educated populace. Ignorance is the handmaiden of tyranny.
That’s why enslavers prohibited enslaved people from learning to read. Fascists burn books. Tyrants close universities.
In their quest to destroy democracy, Trump, Vance and their cronies are intent on shutting the American mind.
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Robert Reich, a former US secretary of labor, is a professor of public policy emeritus at the University of California, Berkeley. He is a Guardian US columnist. His newsletter is at robertreich.substack.com
Education
Release of NAEP science scores
The repercussions from the decimation of staff at the Education Department keep coming. Last week, the fallout led to a delay in releasing results from a national science test.
The National Assessment of Educational Progress (NAEP) is best known for tests that track reading and math achievement but includes other subjects too. In early 2024, when the main reading and math tests were administered, there was also a science section for eighth graders.
The board that oversees NAEP had announced at its May meeting that it planned to release the science results in June. But that month has since come and gone.
Why the delay? There is no commissioner of education statistics to sign off on the score report, a requirement before it is released, according to five current and former officials who are familiar with the release of NAEP scores, but asked to remain anonymous because they were not authorized to speak to the press or feared retaliation.
Related: Our free weekly newsletter alerts you to what research says about schools and classrooms.
Peggy Carr, a former Biden administration appointee, was dismissed as the commissioner of the National Center for Education Statistics in February, two years before the end of her six-year term set by Congress. Chris Chapman was named acting commissioner, but then he was fired in March, along with half the employees at the Education Department. The role has remained vacant since.
A spokesman for the National Assessment Governing Board, which oversees NAEP, said the science scores will be released later this summer, but denied that the lack of a commissioner is the obstacle. “The report building is proceeding so the naming of a commissioner is not a bureaucratic hold up to its progress,” Stephaan Harris said by email.
The delay matters. Education policymakers have been keen to learn if science achievement had held steady after the pandemic or tumbled along with reading and math. (Those reading and math scores were released in January.)
The Trump administration has vowed to dismantle the Education Department and did not respond to an emailed question about when a new commissioner would be appointed.
Researchers hang onto data
Keeping up with administration policy can be head spinning these days. Education researchers were notified in March that they would have to relinquish federal data they were using for their studies. (The department shares restricted datasets, which can include personally identifiable information about students, with approved researchers.)
But researchers learned on June 30 that the department had changed its mind and decided not to terminate this remote access.
Lawyers who are suing the Trump administration on behalf of education researchers heralded this about-face as a “big win.” Researchers can now finish projects in progress.
Still, researchers don’t have a way of publishing or presenting papers that use this data. Since the mass firings in mid-March, there is no one remaining inside the Education Department to review their papers for any inadvertent disclosure of student data, a required step before public release. And there is no process at the moment for researchers to request data access for future studies.
“While ED’s change-of-heart regarding remote access is welcome,” said Adam Pulver of Public Citizen Litigation Group, “other vital services provided by the Institute of Education Sciences have been senselessly, illogically halted without consideration of the impact on the nation’s educational researchers and the education community more broadly. We will continue to press ahead with our case as to the other arbitrarily canceled programs.”
Pulver is the lead attorney for one of three suits fighting the Education Department’s termination of research and statistics activities. Judges in the District of Columbia and Maryland have denied researchers a preliminary injunction to restore the research and data cuts. But the Maryland case is now fast-tracked and the court has asked the Trump administration to produce an administrative record of its decision making process by July 11. (See this previous story for more background on the court cases.)
Related: Education researchers sue Trump administration, testing executive power
Some NSF grants restored in California
Just as the Education Department is quietly restarting some activities that DOGE killed, so is the National Science Foundation (NSF). The federal science agency posted on its website that it reinstated 114 awards to 45 institutions as of June 30. NSF said it was doing so to comply with a federal court order to reinstate awards to all University of California researchers. It was unclear how many of these research projects concerned education, one of the major areas that NSF funds.
Researchers and universities outside the University of California system are hoping for the same reversal. In June, the largest professional organization of education researchers, the American Educational Research Association, joined forces with a large coalition of organizations and institutions in filing a legal challenge to the mass termination of grants by the NSF. Education grants were especially hard hit in a series of cuts in April and May. Democracy Forward, a public interest law firm, is spearheading this case.
Contact staff writer Jill Barshay at 212-678-3595, jillbarshay.35 on Signal, or barshay@hechingerreport.org.
This story about delaying the NAEP science score report was written by Jill Barshay and produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for Proof Points and other Hechinger newsletters.
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