The question matters because men’s basketball occupies a peculiar position in the economics of the National Collegiate Athletic Association. The sport generates billions of dollars in television and ticket revenue, anchored by the annual NCAA tournament, yet the players who produce that value receive no direct salary. Graduation success rate, the metric the NCAA introduced in the early 2000s, was designed to hold programs accountable for the academic outcomes of their athletes. Unlike the federal cohort graduation rate, which counts only athletes who graduate within six years of enrollment at the same institution, the graduation success rate credits transfers who graduate elsewhere and includes athletes who leave in good academic standing. It is therefore a more forgiving but also more comprehensive measure of whether a program is genuinely educating the young people it recruits.

The economic logic of why NIL might affect graduation rates runs in both directions, which is precisely what makes the empirical question interesting. On one side, the opportunity cost of studying has risen. A player with a large social media audience or a marketable personal brand faces a real trade-off: an hour spent filming promotional content or negotiating with a collective is an hour not spent in a study hall. If the marginal return to brand-building exceeds the perceived marginal return to a degree, rational agents may reallocate time away from academics. The explosion of the transfer portal has compounded this effect, since players who change schools multiple times in search of better compensation face disrupted credit accumulation, lost progress toward major requirements, and in some cases semester gaps that push graduation further away.

On the other side, several mechanisms push in the opposite direction. NIL income reduces financial pressure that has historically pushed some athletes to leave school early for professional contracts overseas or in the NBA G League. A player earning meaningful endorsement money may now be able to remain enrolled, complete a degree, and still support his family, something the amateur model never allowed. Boosters and collectives, whose contributions now flow partly through universities, have an institutional interest in keeping eligible players eligible, and academic eligibility rules still require satisfactory progress toward a degree. Programs with lucrative NIL ecosystems may therefore invest more heavily in academic support, tutoring, and degree-completion programs, not out of altruism but because a player who flunks out is a player who cannot play. In principle, then, NIL could raise graduation rates even as it lowers study time, and disentangling these forces requires careful data analysis.

This is the analytical terrain that Messina and Sommers set out to map. Their study, accepted by the Atlantic Economic Journal in September 2026 and published online on the first of October, examines graduation success rates across Division I men’s basketball programs in the period surrounding the introduction of NIL rights. The authors, both affiliated with the Department of Economics at Middlebury College, bring to the question a long tradition of sports economics scholarship that treats college athletics as a laboratory for studying incentives, human capital, and labor markets. The paper includes supplementary material detailing its data construction and statistical procedures, and the authors acknowledge comments from Annabelle Fowler in preparing the final version.

The methodological challenge such studies face is substantial, and it is worth spelling out why. Graduation success rates are reported annually by the NCAA for each program, but they are rolling four-year cohort measures, which means any effect of NIL, introduced in 2021, would only fully appear in data released several years later. A player who enrolled in 2021, the first NIL class, would graduate by 2027 at the earliest, so the earliest post-NIL GSR figures blend pre-policy and post-policy cohorts. Researchers must therefore be careful about attributing any observed change to NIL alone. Confounding factors abound: the COVID-19 pandemic disrupted academic life for cohorts graduating in the early 2020s, the NCAA granted an extra year of eligibility that extended many careers, and the transfer portal, which predates NIL but accelerated alongside it, independently reshapes graduation statistics by moving students between institutions.

There is also a deeper conceptual issue about what graduation success rates actually measure in the NIL era. The metric was designed for a world in which the primary exit option for a talented underclassman was declaring for the professional draft. Today, a player who leaves school after two years to pursue a full-time career as a paid athlete, whether through lucrative collectives, professional opportunities, or personal branding, counts against his original program’s rate unless he graduates elsewhere in good standing. Some economists would argue that this makes GSR an increasingly poor welfare measure: a player who forgoes a low-completion-probability degree for genuine earning opportunities may be behaving optimally, not failing. Others counter that the vast majority of basketball players will never earn a living from the sport, and that for them the degree remains the single most valuable asset college athletics confers, which is why any erosion of academic outcomes would represent a genuine social cost.

The stakes extend beyond basketball. Football has followed a similar trajectory, and the legal environment continues to evolve, with litigation challenging NCAA compensation rules and several states enacting or revising their own NIL statutes. If rigorous empirical work finds that graduation rates have held steady or improved, it would strengthen the case that athlete compensation and academic mission can coexist, and would undercut arguments for rolling back NIL rights. If the data instead reveal a systematic decline, particularly concentrated at programs with the most aggressive collective spending, it would give regulators and university presidents concrete evidence that the current unregulated market imposes academic costs, potentially justifying eligibility-linked compensation structures or enhanced academic support mandates. Either result would be informative, which is why studies of this kind are likely to multiply as cleaner post-2021 data become available.

What the Messina and Sommers paper ultimately contributes is a disciplined economic framing of a debate that has so far been dominated by anecdote. Coaches lament players who treat one-year stints as paid stopovers; athletic directors tout rising academic scores; commentators assert that money has ruined the student-athlete ideal. None of these claims, on its own, constitutes evidence. By assembling program-level graduation data spanning the transition into the NIL era and applying standard statistical tools to detect whether the policy shift coincided with a change in academic outcomes, the study converts a loud argument into a testable hypothesis. The answer it reaches will be scrutinized, replicated, and extended, but the mere existence of such analysis marks a maturing of the conversation around athlete compensation.

For the young men who dunk on national television each winter, the stakes are personal and immediate. College basketball players graduate at lower rates than most of their classmates, and the sport’s one-and-done culture has long strained the fiction that every scholarship athlete is primarily a student. NIL has added money to that equation without adding clarity. Whether endorsement deals become a bridge that helps more players finish their degrees, or a current that sweeps them out of the classroom and into a precarious gig economy of paid appearances, is an empirical question that economists are only now beginning to answer. The Middlebury study is an early and careful entry in what will surely become one of the defining research literatures of the post-amateur era of college sport, and its central question, whether dollars and diplomas can coexist on the hardwood, will shape policy debates for years to come.

Subject of Research: The effect of name, image, and likeness compensation on graduation success rates in Division I men's college basketball

Article Title: Has NIL Affected Graduation Success Rates at College Basketball Programs?

Article References: Messina, C. G., & Sommers, P. M. (2026). Has NIL Affected Graduation Success Rates at College Basketball Programs?. Atlantic Economic Journal. https://doi.org/10.1007/s11293-026-09863-5

Image Credits: AI Generated

DOI: 10.1007/s11293-026-09863-5

Keywords: NIL, college basketball, graduation success rate, NCAA, student-athletes, sports economics, transfer portal, athlete compensation, academic outcomes, Division I athletics, Middlebury College, Atlantic Economic Journal

The choice to restrict the synthesis to randomized controlled trials is what sets this analysis apart from the growing pile of AI-in-education reviews. Randomization is the gold standard for causal inference because it ensures that, on average, students assigned to AI-supported instruction differ from control students only by chance. Earlier syntheses that mixed randomized and non-randomized studies risked inflating effects through self-selection: enthusiastic early adopters of AI tools are not typical learners. By filtering out such designs, the authors aimed to answer a genuinely causal question about what happens when AI is introduced into a learning environment, rather than merely correlating AI use with outcomes that could be driven by motivation, resources, or teacher quality.

The headline result concerns academic outcomes. Across 37 studies, AI interventions produced a pooled effect of Hedges’ g = 0.68, a medium-to-large effect by conventional benchmarks in educational psychology, statistically significant at p < 0.001. In practical terms, a student at the 50th percentile of a control group would be expected to rise to roughly the 75th percentile after receiving an AI-based intervention, on average. That is a substantial gain, comparable to some of the more effective conventional educational interventions, and it suggests that AI tutoring systems, chatbot-based feedback tools, and generative AI writing assistants can meaningfully move the needle on grades, test scores, and subject-specific performance when deployed under controlled conditions.

The picture for social-emotional outcomes is more tentative. The analysis focused on negative social-emotional outcomes such as anxiety, stress, and loneliness, and found a directionally favorable effect of Hedges’ g = −0.37 across 11 studies, meaning AI interventions tended to reduce these negative states. However, with p = 0.058, the estimate fell just short of conventional statistical significance. The authors interpret this as a promising signal rather than a proven benefit. The finding aligns with a parallel literature on AI chatbots for mental health, which has shown that conversational agents can reduce symptoms of anxiety and depression in some populations, but the evidence base within educational settings remains thin, and the possibility of publication bias or small-study effects cannot be excluded from a pool of only 11 trials.

Most provocative is the cognitive result. Despite the marketing promise of AI tools that sharpen thinking, the pooled effect on cognitive outcomes was a small and statistically nonsignificant g = 0.17 across 11 studies, with p = 0.480. This gap between academic and cognitive gains echoes a recurring theme in cognitive science: performance on a trained task can improve without durable improvements in the underlying mental machinery. Researchers studying cognitive offloading have documented how outsourcing memory and reasoning to external tools can boost immediate performance while diminishing unaided ability, and studies of generative AI have reported reduced mental effort alongside shallower inquiry. The meta-analysis cannot adjudicate these mechanisms directly, but its pattern of results is consistent with the hypothesis that AI helps students produce better work in the short term without necessarily making them stronger thinkers.

The dose–response analysis is the methodological centerpiece of the paper and its most consequential practical finding. Using restricted cubic splines, a flexible regression technique that models curved relationships without imposing a rigid functional form, the team mapped academic outcomes against cumulative intervention duration. The resulting curve rose steeply in the early hours of AI exposure, peaked at approximately 22.79 hours, and then flattened with a slight downward drift. This shape challenges the intuitive assumption that more AI-supported learning time is monotonically better. It instead echoes findings from other domains, including research on distributed practice and feedback frequency, where benefits accrue rapidly at first and then saturate or even reverse as learners reach diminishing returns or begin to over-rely on the scaffold.

Why might the curve bend downward? Several mechanisms are plausible. Early hours of AI interaction may deliver the highest-value support: immediate feedback, personalized pacing, and adaptive difficulty that a single teacher managing dozens of students cannot provide. Beyond that point, marginal gains shrink as the material most amenable to AI support is mastered. Prolonged exposure may also encourage cognitive offloading, with students delegating effortful thinking to the machine rather than engaging in the desirable difficulties that consolidate learning. The authors are careful to note that the spline estimate is descriptive and that the trials contributing to the upper end of the duration range are relatively few, so the post-peak decline should be read as a trend warranting further study rather than a precise prescription.

Moderator analyses added important texture to the headline numbers. The effects of AI interventions varied depending on whether the technology was accompanied by human assistance, suggesting that hybrid human–AI configurations, in which teachers orchestrate and supplement the technology, may perform differently from fully autonomous deployments. Effects also differed across educational levels, indicating that what works for university students may not transfer cleanly to younger learners or adult students. These moderators matter because they imply there is no single ‘AI effect’ to be discovered; the technology’s value depends on instructional context, the role of the human teacher, and the developmental stage of the learner. This aligns with a broader movement in the learning sciences toward hybrid human–AI learning technologies in which each partner contributes complementary strengths.

The study’s transparency strengthens its credibility. The protocol was pre-registered, and the registration, data, and analysis code are openly available on the Open Science Framework, allowing independent researchers to verify the pooled estimates and rerun the models. The team employed standard meta-analytic safeguards, including assessments of risk of bias in the included randomized trials and checks for publication bias, and used robust variance estimation methods to handle dependent effect sizes arising when single studies report multiple outcomes. Funding came from the National Natural Science Foundation of China and university research funds, and the authors declared no conflicts of interest.

For educators and policymakers, the message is nuanced but actionable. AI interventions can deliver real academic gains, with an average effect large enough to matter for curriculum planning, but the evidence does not yet support claims that these tools enhance raw cognitive ability, and their emotional benefits remain provisional. The dose–response curve argues for deliberate, bounded deployment rather than unlimited screen time: the first twenty-odd hours of well-designed AI-supported instruction appear to capture most of the value. As generative AI continues to diffuse through schools at remarkable speed, this synthesis provides something the debate has lacked, a cautious, outcome-based map of where the technology genuinely helps, where it merely helps students look helped, and where the returns begin to fade.

Subject of Research: Meta-analysis of randomized controlled trials on the effects of AI interventions in education across cognitive, social-emotional, and academic outcomes

Article Title: Effects of AI Interventions Across Cognitive, Social-emotional, and Academic Outcomes: a Meta-analysis of Randomized Controlled Trials with Nonlinear Dose–response Evidence

Article References: Ren, X., Li, X., Gao, L., Wei, Q., Liu, H., & Yu, X. (2026). Effects of AI Interventions Across Cognitive, Social-emotional, and Academic Outcomes: a Meta-analysis of Randomized Controlled Trials with Nonlinear Dose–response Evidence. Educational Psychology Review, 38(1), Article 128. https://doi.org/10.1007/s10648-026-10217-5

Image Credits: AI Generated

DOI: 10.1007/s10648-026-10217-5

Keywords: artificial intelligence, education, meta-analysis, randomized controlled trials, academic outcomes, cognitive outcomes, social-emotional outcomes, dose-response, generative AI, educational psychology, cognitive offloading, instructional technology

The research, conducted by Haniyeh Shakery Chenary, Kolsoum Akbarnataj Bisheh, and Ebrahim Mohammadian Akerdi, recruited 54 students with dyslexia aged 8 to 12 years from learning disorder centers in Babol, a city in northern Iran. The children, both boys and girls, were selected according to inclusion criteria using a convenience sampling method and then randomly assigned to one of three groups of 18 participants each: a cognitive-behavioral play therapy group, a cognitive rehabilitation group, and a control group that received no intervention. The study followed a pre-test, post-test design with a one-month follow-up, allowing the researchers to track not only whether the treatments worked immediately but whether their effects persisted.

To measure executive functions, the team used the Brief Executive Function Behavioral Rating Questionnaire, an instrument designed to capture everyday manifestations of executive control as observed in children’s behavior. Executive functions are typically broken down into core components: inhibitory control, the ability to suppress impulsive responses; working memory, the capacity to hold and manipulate information in mind; and cognitive flexibility, the skill of shifting between tasks or mental sets. In children with dyslexia, deficits in these domains are well documented and are thought to compound the phonological processing difficulties that define the disorder, making reading acquisition and academic performance even harder.

The results were striking in their symmetry. After the interventions, post-test scores for executive functions decreased significantly in both treatment groups—lower scores on this behavioral rating measure indicating fewer executive function problems, and therefore improvement. Statistical analysis using repeated-measures analysis of variance confirmed that these changes were highly significant, with p values below .001, and crucially, the improvements were maintained at the one-month follow-up. The control group, by contrast, showed no comparable change. In practical terms, both therapies appeared to equip children with better self-regulation, attention, and mental control, and those benefits did not evaporate once the sessions stopped.

Perhaps the most intriguing finding, however, was what the researchers did not find. When the two intervention groups were compared head to head, no statistically significant difference emerged between them (p > .05). Cognitive-behavioral play therapy, with its emphasis on emotional expression and behavioral learning through play, proved just as effective as cognitive rehabilitation, a more direct, exercise-based approach to training cognitive skills. This equivalence matters, because it suggests that clinicians and schools may be able to choose the modality that best fits a particular child, setting, or budget without sacrificing therapeutic benefit.

The mechanisms behind the two approaches differ in fascinating ways. Cognitive rehabilitation is grounded in neuropsychology: it involves repeated, structured practice on tasks designed to strengthen specific cognitive operations, much like physical therapy targets particular muscle groups. Modern implementations range from paper-and-pencil exercises to computerized and even virtual reality-based programs, and the approach has been applied across populations from stroke survivors to children with learning disabilities. The underlying premise is neuroplasticity—that targeted, progressive challenge can reshape the efficiency of the neural networks supporting attention and memory.

Cognitive-behavioral play therapy, by contrast, marries the principles of cognitive-behavioral therapy with the natural language of childhood: play. Rather than asking an 8-year-old to sit through abstract discussions of thoughts and feelings, the therapist embeds cognitive and behavioral strategies in games, stories, and imaginative scenarios. Through play, children rehearse self-control, practice problem-solving, learn to tolerate frustration, and internalize coping strategies. For children with dyslexia, who frequently experience academic failure, shame, and anxiety around reading, this format may address both the cognitive deficits and the emotional fallout that accompany them. Previous research has linked poor reading to elevated rates of internalizing problems such as anxiety and depression, and to diminished self-concept, making an intervention that works through an enjoyable, low-pressure medium particularly appealing.

The study’s authors frame their findings against a sobering backdrop. Many children with dyslexia face poor academic and career outcomes, and their difficulties extend beyond phonological processing to attention and executive functions. Executive dysfunction in dyslexia has been documented in neuropsychological studies across multiple countries, and deficits in working memory have been shown to correlate with reading performance. Because executive functions also predict broader academic achievement and classroom learning behaviors, improving them may yield dividends that go well beyond reading itself—better task completion, stronger self-regulation, and greater capacity to benefit from reading instruction.

The quasi-experimental design, while robust for a clinical field study, does carry limitations worth noting. The sample was drawn from learning disorder centers in a single city using convenience sampling, which may limit how broadly the findings generalize. The outcome measure relied on a behavioral rating questionnaire rather than direct neuropsychological testing, and the follow-up period was one month, leaving open the question of whether benefits persist over longer horizons. Still, the randomized assignment to groups, the inclusion of a control condition, and the follow-up assessment give the findings a credibility that many small clinical studies lack. The researchers also report that the study protocol and ethics code were approved by the Ethics Committee of Islamic Azad University, Sari Branch, and that caregiver consent and child assent were obtained before data collection.

For clinicians, educators, and parents, the takeaway is refreshingly practical: two effective, accessible, and fundamentally different treatments can improve the executive functions of children with dyslexia, and neither holds a decisive advantage. Cognitive rehabilitation offers a targeted, skills-drilling route for settings equipped with structured programs, while cognitive-behavioral play therapy offers a developmentally sensitive, emotionally attuned alternative that children may find inherently motivating. The authors conclude that both can be used as effective and accessible therapeutic approaches for treating students with dyslexia. In a field where families often face long waiting lists and expensive specialized services, evidence that two distinct pathways lead to the same destination is genuinely good news—and a reminder that the developing brain, given the right kind of support, can be nudged toward stronger self-regulation and control.

Subject of Research: Comparing cognitive-behavioral play therapy and cognitive rehabilitation for improving executive functions in students with dyslexia

Article Title: The Effectiveness of Cognitive-Behavioral Play Therapy and Cognitive Rehabilitation on Executive Functions in Students With Dyslexia

Article References: Shakery Chenary, H., Akbarnataj Bisheh, K., & Mohammadian Akerdi, E. (2026). The Effectiveness of Cognitive-Behavioral Play Therapy and Cognitive Rehabilitation on Executive Functions in Students With Dyslexia. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07548-1

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07548-1

Keywords: dyslexia, executive functions, cognitive-behavioral play therapy, cognitive rehabilitation, working memory, inhibitory control, cognitive flexibility, learning disorders, child psychology, intervention study, neuropsychology, academic outcomes

At the heart of the initiative is a simple but consequential premise: brain health is not fixed, and proactive training in how we think can change trajectories at any stage of life. Rather than treating cognitive performance as an innate trait that students either possess or lack, the program treats it as a trainable capacity, one that can be deliberately developed through structured practice, assessment, and feedback. This philosophy reflects the Center for BrainHealth’s broader research mission, which has long focused on translating laboratory findings about neuroplasticity, attention, and reasoning into practical tools that people can apply in classrooms, workplaces, and everyday life.

The practical architecture of the program is deliberately layered. Faculty and staff will receive training on core brain health principles and strategies that they can then reinforce with students, with the added benefit of applying the same techniques to their own cognitive lives. This train-the-trainer model is significant from a research standpoint: it embeds brain health concepts into the daily interactions of the campus rather than confining them to occasional seminars. Students, meanwhile, will take part in tailored workshops targeting three higher-order cognitive capacities that the Center for BrainHealth has studied extensively: strategic attention, integrated reasoning, and innovation. These are the same domains emphasized in the center’s SMART brain health training, a strategy-based toolkit developed and tested by researchers over roughly thirty years.

Beyond the core cognitive curriculum, the initiative addresses the affective and social dimensions of student life. Topic-driven sessions will tackle stress management, mindfulness, and social connection, recognizing that emotional regulation and interpersonal bonds are not peripheral to academic achievement but tightly intertwined with it. Decades of research in cognitive neuroscience and psychology have linked chronic stress to impaired working memory and reduced prefrontal efficiency, while social isolation has been associated with measurable declines in cognitive health across the lifespan. By folding these topics into a single campus-wide program, the partnership acknowledges that thinking well depends on living well, and that a student’s capacity for complex reasoning cannot be separated from their emotional and social circumstances.

Measurement is another defining feature of the initiative. Participants will have access to regular brain health assessments, allowing them to track changes in cognitive performance over time rather than relying on subjective impressions. This longitudinal approach mirrors the methodology of The BrainHealth Project, the center’s landmark study launched in 2020, which aims to identify actionable strategies for optimizing brain health throughout life and to clarify the dynamic relationships among lifestyle factors, biological markers, brain training, and cognitive performance. The center’s proprietary BrainHealth Index, a measure designed to chart an individual’s upward or downward brain health trajectory from whatever their starting point, exemplifies this philosophy of treating brain fitness as a dynamic, modifiable trajectory rather than a static snapshot.

To ensure the program outlives its initial rollout, the partnership includes ongoing access to self-directed online training and application tools through the BrainHealth Platform. Sustainability is a persistent challenge for campus wellness initiatives, which often flourish briefly and then fade when grant cycles end or institutional priorities shift. By building digital, self-paced resources into the program’s foundation, the collaborators are attempting to convert a structured intervention into a durable part of the campus culture, one that students and employees can return to throughout their time at the college and beyond.

Leaders on both sides have framed the partnership in terms that go well beyond test scores. Lori Cook, PhD, CCC-SLP, director of clinical research at the Center for BrainHealth, said the initiative builds on years of collaboration with Dallas College and reflects more than three decades of cognitive neuroscience research demonstrating that it is never too soon to become proactive about the brain’s fitness and performance. By integrating evidence-based tools and strategies into the campus experience, she noted, the program equips students as well as faculty and staff with actionable ways to become more strategic and intentional about how they think, manage challenges, and thrive in an increasingly complex world.

Dallas College administrators have emphasized the whole-student dimension of the effort. Thaddeus Mantaro, PhD, dean of student health and wellness, said the college is deeply committed to supporting students academically, emotionally, and socially, and described the partnership as bringing innovative, science-backed resources directly to the community, with optimism about its potential to enhance student well-being, strengthen academic success, and contribute to improved retention. Carlos Cruz, EdD, associate vice chancellor of the college, added that Dallas College recognizes students as more than learners, understanding that academic success is closely linked to personal growth, emotional well-being, and overall health. Retention, in particular, is a high-stakes metric for community colleges, where students often balance coursework with work and family obligations, and where even modest improvements in persistence can translate into substantially better life outcomes.

The scale of the institution involved gives the initiative considerable reach. Founded in 1965, Dallas College operates seven campuses—Brookhaven, Cedar Valley, Eastfield, El Centro, Mountain View, North Lake, and Richland—along with a dozen centers across Dallas County. As one of the largest community colleges in the United States, it serves more than 133,000 credit, workforce, and continuing education students annually, offering associate degrees and career and technical certificates in more than 100 areas of study, as well as bachelor’s degrees in education, nursing, software development, and management. It is also the largest provider of dual credit in Texas, serving 30,000 high school students through 64 dual credit programs. That breadth means the BrainHealthy Campus framework will be tested across an unusually diverse population, from teenagers earning college credit to working adults retraining mid-career.

Financial support for the initiative comes from philanthropic sources, with grants from The Meadows Foundation and The Hoglund Foundation fueling the program at Dallas College. The collaboration also arrives at a moment when the Center for BrainHealth is broadening its regional footprint, having recently appointed new members to its 2026-27 Advisory Board, drawing leaders from business, health care, science, technology, real estate, and philanthropy who share a commitment to advancing brain health across North Texas and beyond. For researchers, the partnership offers a real-world laboratory for studying how proactive cognitive training performs at scale in a post-secondary setting, a context that differs markedly from the controlled cohorts of typical laboratory studies. For the tens of thousands of students who pass through Dallas College each year, it offers something rarer: a systematic, science-driven attempt to treat the brain itself as the most important asset a learner brings to campus, and one worth training as deliberately as any subject on the syllabus.

Subject of Research: A research partnership applying cognitive neuroscience-based brain health training and assessment to improve student well-being and academic outcomes at a large community college.

Article Title: New BrainHealthy Campus initiative aims to strengthen community brain health and enhance academic outcomes

Article References: New BrainHealthy Campus initiative aims to strengthen community brain health and enhance academic outcomes. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: brain health, cognitive training, Center for BrainHealth, Dallas College, higher education, student well-being, academic outcomes, neuroscience, SMART training, BrainHealth Index, retention, community college