Higher education is at a pivotal moment where emerging technologies can redefine the learning experience. Artificial intelligence (AI) offers powerful tools for personalized education – tailoring content, pace, and feedback to each learner’s needs. This blog post explores how faculty and edtech developers can harness AI to create adaptive, student-centered learning environments. We’ll delve into Klover.ai’s visionary frameworks – Artificial General Decision-Making (AGD™), Point of Decision Systems (P.O.D.S.™), and Graphic User Multimodal Multiagent Interfaces (G.U.M.M.I.™) – and see them in action through real-world case studies. Backed by academic research and practical examples, we provide a strategic, technically rigorous roadmap for using AI agents and multi-agent systems to transform education.

The Push for Personalized Learning with AI

In today’s universities and schools, instructors face cohorts of students with widely varying backgrounds, learning speeds, and interests. Traditional one-size-fits-all teaching often leaves some learners unengaged or unchallenged. The need for personalized learning – adapting educational content and support to individual students – has never been greater. AI now provides the means to deliver this personalization at scale​.

For higher education faculty, AI can act as an intelligent assistant – automating routine tasks, analyzing student data for insights, and even providing 1:1 tutoring for students. For edtech developers, new AI multi-agent systems and consulting frameworks make it possible to build adaptive learning platforms that continuously refine the experience for each user. The goal is a client transformation in education: leveraging enterprise automation concepts to make learning environments as responsive and data-driven as modern businesses.

Klover.ai’s approach to this challenge is grounded in decision intelligence. Rather than pursuing AI that replaces human educators, Klover focuses on AI that augments human decision-making. In what we term Artificial General Decision-Making (AGD™), ensembles of specialized AI agents work together to help people (students, instructors, administrators) make better decisions in context. We’ll examine how AGD™ underpins personalized learning, how P.O.D.S.™ multi-agent systems enable real-time adaptation in the classroom, and how G.U.M.M.I.™ interfaces turn complex AI outputs into intuitive digital solutions for users. Throughout, we maintain a people-centered perspective: successful adoption of AI in education requires keeping educators “in the loop” and aligning with pedagogical goals, not just deploying technology for its own sake.

AGD™ and Data-Driven Personalization in Education

One key to personalization is data – every click, quiz, and question in a digital learning environment yields information about a student’s understanding. However, making sense of this massive data in real time is beyond human capability. This is where Artificial General Decision-Making (AGD™) comes in. At Klover.ai, we define AGD™ as the use of context-aware AI decision engines to augment human decision-making. In an educational setting, AGD means AI systems crunch through large volumes of student data and provide adaptive, situation-specific recommendations to guide learning. Instead of a monolithic AI tutor, AGD relies on a network of specialized AI agents (for content, assessment, engagement, etc.) cooperating to support each student’s success​. The aim is not artificial general intelligence replicating a teacher, but decision intelligence that helps instructors and learners make optimal choices moment-to-moment.

How does AGD™ enable personalized learning?

It starts by integrating diverse data sources – prior academic records, real-time quiz results, engagement metrics from an LMS, even behavioral cues – into a learner profile. AI decision models then use this profile to identify what each student should do next for maximum growth. For example, an AGD-driven system might decide that a particular student who struggled with last week’s math problems should revisit a foundational concept before moving on. Another student, already proficient, might be fast-tracked to advanced challenges. Crucially, these decisions are context-aware: the AI takes into account why a student is struggling (e.g. lack of prerequisite knowledge vs. lack of motivation) and adapts its recommendations accordingly.

Real-world implementations of data-driven decision systems illustrate the impact. Predictive analytics in higher education is a form of AGD that has shown impressive results. Georgia State University famously deployed an AI-enhanced advising system that monitors each student’s performance across 800 risk factors daily​. When the system detects warning signs (for instance, low grades in a key prerequisite course), it alerts advisors to intervene with the student. This AGD-like approach – using AI to inform human advisors at the point of decision – led to dramatic gains in student success. 

Over a decade, Georgia State boosted its six-year graduation rate by over 20 percentage points, and eliminated equity gaps in graduation between minority and white students (Dimeo, 2017). Advisors conducted tens of thousands of extra proactive meetings with at-risk students each year based on the AI alerts, turning data insights into individualized support​. The decision intelligence provided by AI enabled timely, personalized interventions at scale that would have been impossible with manual faculty effort alone.

Key capabilities of AGD™ in education:

• Predictive Insights for Intervention: AGD™ systems can analyze patterns from millions of past student records to predict which current students are at risk of failing or dropping out. This allows educators to intervene early with targeted support. For example, if a model sees that students who struggle in Intro to Chemistry often fail to graduate in STEM majors, it can flag current students in that situation so instructors or tutors can adjust their approach.
• Personalized Learning Pathways: By considering each learner’s strengths, weaknesses, and progress, AGD™-driven platforms recommend personalized learning pathways. An AI agent might decide that Student A should review Chapter 2 again with different examples, while Student B is ready to skip ahead to Chapter 4. Research shows that such tailoring of content can improve learning outcomes – students progress more rapidly when instruction is matched to their current level.
• Continuous Improvement Through Data: AGD™ isn’t a static rule-based system; it continually refines its decisions through feedback. As more data on what strategies work (or don’t work) for each learner become available, the AI agents update their decision policies. In essence, the system “learns” the optimal way to teach you. This ties into the idea of modular AI – multiple decision models can be updated or swapped out without overhauling the whole system, continuously improving the personalization over time.

Data-driven decision-making provides the brain of an AI-personalized learning environment. But to put those decisions into action in real time, we need a responsive infrastructure. Unlike Artificial General Intelligence (AGI), which aims to replace human cognitive processes entirely, (AGD™) is built to enhance and guide human learning without displacing it. AGI removes the need for humans to learn at all; AGD™, by contrast, ensures learners remain central, using AI to illuminate the best next steps while preserving human agency and intellectual growth.

Adaptive Multi-Agent Systems (P.O.D.S.™) 

A single AI system, no matter how advanced, can be overwhelmed trying to handle all aspects of a live classroom or online course simultaneously. This is why multi-agent systems are emerging as a cornerstone of personalized learning technology. Point of Decision Systems (P.O.D.S.™) refer to orchestrated ensembles of AI agents that collaborate to deliver real-time adaptation and rapid responses at each decision point in the learning process. In simpler terms, P.O.D.S. is a swarm of specialized AIs – we can think of them as AI teaching assistants – each focusing on a specific task, communicating with each other, and collectively ensuring the student’s learning experience is continuously optimized​.

Real-Time Personalization at Scale

Imagine an intelligent tutoring system in which one agent presents instructional content, another agent analyzes the student’s responses to gauge understanding, a third agent monitors the student’s engagement (e.g., detecting if they are frustrated or disengaged), and yet another agent decides when to give hints or adjust the difficulty. These agents operate in parallel and exchange information. For instance, if the engagement agent senses the student is losing interest, it can signal the content agent to introduce a more gamified exercise or the hint agent to step in and encourage the learner. This multi-agent architecture brings a level of adaptability and responsiveness that monolithic systems lack. Each agent can be tuned or trained for its niche – together forming an intelligent automation ecosystem for learning.

In a multi-agent personalized learning ecosystem, distinct AI agents work in concert to support the learner and educator. For example, as illustrated above, an “AI Tutor Agent” interacts with the student by fielding questions and offering guidance, a “Content Agent” provides updated learning materials tailored to the curriculum and student needs, and an “Analytics Agent” crunches performance data to generate insights for the educator. Meanwhile, an “Interface Agent” ensures all this information is delivered through a user-friendly dashboard or multimodal interface. Each agent operates semi-autonomously, but they share data and updates – enabling informed, coordinated decisions in real time at each point of instruction. This multi-agent orchestration is key to scaling personalized learning to many students at once without overwhelming the human teacher​.

Benefits of P.O.D.S.™ multi-agent systems in education:

• Real-Time Adaptation: Multi-agent systems can adjust the learning experience instantly based on student actions. For example, as soon as a student submits an answer, the assessment agent evaluates it and informs the content agent whether to proceed to the next topic or provide remediation. This happens in seconds, maintaining a flow state for learners. Studies have found that such immediate feedback loops boost student engagement and achievement  – learners feel the instruction is responsive to their needs, much like a one-on-one tutor would be.
• Specialization and Parallelism: By dividing roles among agents (content delivery, feedback, monitoring, etc.), P.O.D.S. leverages specialized AI models for each task. One agent might be a language model tuned to answer student questions, while another is a recommender system deciding the next exercise. This specialization improves the quality of each function (each agent is an expert in its domain) and allows multiple actions to happen in parallel. The result is a seamless experience: the student gets a hint from one agent while another is already crunching data to select the next problem.
• Scalable Mentorship: Multi-agent ensembles effectively provide each student with a team of “always-on” mentors. In a large lecture or a massive online course, it’s impractical for one instructor to personalize for everyone. P.O.D.S. offers a way to scale personalization – every learner receives tailored support, whether there are 30 students or 3,000 students in the class. This is how enterprises use multi-agent AI for scaling complex tasks, and education can leverage the same enterprise change: transforming classrooms with a network of AI helpers ensuring no student falls through the cracks.
• Rapid Response and Intervention: The “Point of Decision” emphasis means the system responds at the pivotal moment. If a student is about to make a wrong move in a virtual lab, the AI can step in with a prompt. If a quiz result indicates a misconception, the system immediately offers a clarifying micro-lesson. This agility in response can prevent small learning issues from compounding. A recent survey of AI in classrooms confirms teachers value AI tools that react in real time to student needs, enabling them to intervene more effectively.

Of course, building such multi-agent systems requires robust architecture and careful design – agents must communicate efficiently and share a common goal (supporting the learner). This is where Klover.ai’s P.O.D.S.™ framework provides a blueprint, drawing on proven consulting frameworks for multi-agent coordination. For instance, ensuring all agents reference a unified student model (a shared representation of the learner’s state) is critical for consistency. When well-implemented, P.O.D.S. becomes the dynamic engine driving each student’s personalized journey.

G.U.M.M.I.™ Interfaces

For AI-driven personalized learning to succeed, educators and students must be able to interact with the AI system naturally and trust its outputs. Graphic User Multimodal Multiagent Interfaces (G.U.M.M.I.™) are Klover.ai’s answer to this need. In essence, G.U.M.M.I.™ tools are modular, user-friendly interfaces that gather the streams of information from various AI agents and present them in a coherent, human-readable form. They can include visual dashboards, conversational chatbots, alert systems, and other multimodal elements (text, graphics, speech) that allow users to both monitor and guide the AI-driven learning process.

Think of a professor’s dashboard in an AI-enabled course: rather than wading through raw data, the instructor sees a clear visual summary of which students are excelling and which are struggling, which topics have been mastered class-wide and which need revisiting. The interface might highlight, “Section 2: Calculus – 30% of students scored below 70% on the last quiz, consider reviewing derivatives,” and even suggest specific remedial content. If the system (via the analytics agent) detects a particular student is at risk of falling behind, the interface can raise a real-time alert. The instructor can then drill down – with a click, open that student’s profile to see what misconceptions are identified by the AI. This kind of decision support interface exemplifies G.U.M.M.I.™: complex AI analyses distilled into actionable insights for the educator.

Turning AI Insights into Action

For students, multimodal interfaces enhance engagement. AI can be accessible through a chat-style personal tutor that answers questions in natural language, or through interactive visualizations of their own progress. For example, some adaptive learning platforms include gamified dashboards where students see their “learning path” charted out, with AI-driven recommendations on what to tackle next. Others use voice assistants to provide on-demand help (“Hey tutor, I’m stuck on this step – any hints?”). 

Research shows that such interfaces, which allow students to converse with AI or receive feedback in multiple forms, can increase time-on-task and motivation. In one study, students reported feeling more supported when a chatbot could answer their questions instantly, even outside of class hours, effectively providing 24/7 tutoring support.

Core principles of effective G.U.M.M.I.™ design:

• Clarity and Intuition: The interface should translate AI outputs into intuitive visuals or messages. Educators shouldn’t need a data science degree to interpret predictions or recommendations. For instance, an AI risk score for a student could be shown as a simple traffic light indicator (green/yellow/red) on the teacher’s dashboard, with a brief explanation. Clarity builds trust – when teachers understand what the AI is saying, they are more likely to act on it.
• Multimodal Feedback: Different users benefit from different modes of information. G.U.M.M.I.™ interfaces deliver content via text, charts, audio, and even haptic feedback when appropriate. An instructor might get an email summary each morning, a visual dashboard during class, and a chatbot on their phone for quick queries. Students similarly might get a mix of written feedback on assignments, video tutorials recommended by the AI, and encouraging voice notes for motivation. Multi-modal AI agents ensure the information finds the user in the format that’s most effective.
• Two-Way Interaction: A hallmark of G.U.M.M.I.™ is that it’s not just about displaying information – it enables the human user to feed back into the system. For example, an instructor can override or adjust AI suggestions through the interface (“I see the system wants to skip Chapter 3 for Alice, but I know she’d benefit from the practice – assign it anyway.”). This keeps the educator in control.
• Explainability and Trust: To encourage adoption, AI systems must explain their reasoning in user-friendly terms. Modern AI agents can provide natural language explanations for their recommendations. A G.U.M.M.I.™ interface might have a feature like, “Why am I seeing this suggestion?” clicking which reveals, for example, “The system suggests re-teaching Topic X because 40% of the class showed low performance in the last activity and similar past cohorts improved after a refresher on Topic X.” Providing such rationale builds trust with educators and also helps debug any errors the AI might have.

In practical terms, implementing G.U.M.M.I.™ could mean augmenting an LMS or edtech app with new UI components. Many current systems are adding AI-powered analytics dashboards – a trend we see growing. Edtech developers should focus not only on the AI models, but equally on the UX design: the goal is a smooth user experience where faculty and students feel empowered, not confused, by the AI. When done right, these interfaces become an essential part of the learning process, guiding users through complex data and enabling actionable decision-making. 

Real-World Case Studies of AI-Personalized Learning

Theory and frameworks are important, but nothing drives the point home better than actual success stories. Here we highlight two case studies – one from K-12 education and one from higher education – where AI-driven personalization made a measurable difference. These cases demonstrate the concepts of AGD™, P.O.D.S™, and G.U.M.M.I™ come together to solve real problems in learning environments.

Case Study 1: K-12 Adaptive Learning Boosts Math Outcomes

Context & Implementation: 

An elementary school math program integrated an adaptive learning platform (DreamBox Learning) into its curriculum. The platform functions as a multi-agent tutor: it presents math exercises, analyzes student responses in real time, and adapts the difficulty and sequence of problems for each child. Teachers receive dashboard updates on student progress. The system essentially acts as a personal AI math coach for every student, aligning with the P.O.D.S™ approach (content agent, assessment agent, etc. working together behind the scenes). The adoption was studied in a large-scale research trial across multiple schools, including public schools in Howard County, MD, and a charter network.

Results: 

The impact was significant. Students who used the AI-driven platform for just around 60 minutes per week saw faster gains in math proficiency than those who did not. In a study by Harvard’s Center for Education Policy Research, average-performing students (50th percentile) who engaged with the adaptive software moved up to approximately the 54th–55th percentile in state math assessments within one school year, outpacing their peers . In practical terms, this means many students mastered concepts that, without personalization, they would have struggled with or not reached until later. Teachers noted improved engagement – the immediate feedback and interactive nature of the AI tutor kept students motivated, even in topics that previously caused frustration. One key finding was that greater usage led to greater gains: students who followed the platform’s recommended usage and lesson recommendations saw the biggest jumps in achievement.

Takeaways: 

This case demonstrates how modular AI agents can dramatically enhance a core skill area like math. The content-delivery and assessment agents in the software fine-tuned the learning path for each child (e.g., giving extra practice on fractions to one student while pushing another ahead to geometry), embodying AGD™’s data-driven decision making. The success also hinged on an effective interface – the platform’s student-facing interface was gamified and intuitive, and the teacher-facing dashboard provided clear insight into each student’s progress, aligning with G.U.M.M.I™ principles. Notably, even though the AI was working autonomously with students, teachers remained essential: they used the AI-generated insights to form small groups, assign targeted homework, and have informed one-on-one coaching sessions. This human-AI collaboration resulted in measurable learning improvements and a more efficient classroom, where each student’s needs were addressed.

Case Study 2: Data-Driven Student Success at Georgia State University

Context & Implementation: 

Georgia State University (GSU) faced a challenge common in large public universities – low graduation rates, especially among first-generation and minority students. Starting in 2012, GSU invested in an AI-powered advising system to personalize support for tens of thousands of students. The system can be viewed as an AGD™ framework applied to academic advising: it aggregates data on each student (academic performance, course registration patterns, financial records, etc.) and uses predictive algorithms (developed from historical student data) to identify risk levels for not completing a degree. This information is delivered through a user-friendly advisor dashboard (the interface component), where advisors are prompted with specific alerts like “Student X is off track in their major” or “Student Y might struggle in Course Z based on past students’ outcomes.” Each alert comes with recommended actions, and advisors log outcomes, feeding back into the data loop. 

Results: 

The introduction of this decision intelligence system transformed student outcomes at GSU. Over the span of about 10 years, GSU’s six-year graduation rate increased by 23 percentage points (Dimeo, 2017) – from the low 30s to well over 50%. This is a remarkable jump for an institution of GSU’s size (50,000+ students) and demographics. What’s more, achievement gaps narrowed considerably: for the past several years, GSU’s African American, Hispanic, and low-income students have graduated at rates equal to or higher than other student groups, an outcome virtually unheard of a decade prior. University leaders credit the AI-enhanced advising platform as a major driver of these changes. The system enabled proactive interventions: in a five-year period, advisors conducted over 200,000 extra advising meetings triggered by AI alerts​. 

Instead of waiting for a student to seek help (often when it’s too late), the university now reaches out to the student as soon as the AI flags an issue – for example, if a student registers for a combination of courses that the data indicates may be too demanding together, or if a student’s GPA dips mid-semester, suggesting they might need tutoring. By meeting students at the point of decision (when they choose courses, when they consider dropping out, etc.), GSU’s multi-agent advising system (the P.O.D.S™ of predictive model + human advisor + communication tools) has kept thousands of students on track. Financially, this meant not only improved student success but also higher enrollment retention for the university (a 1% increase in retention equated to millions in tuition revenue).

Takeaways: 

GSU’s case underscores the power of AI when used to augment human expertise. The predictive analytics agent analyzed data far beyond human capacity, but it was the Advisor–AI partnership that made the difference. Advisors frequently mention how the interface – a color-coded dashboard and alert system – allowed them to prioritize students in need and gave them conversation starters for outreach (e.g., “I saw you did not pass Math 101 – let’s discuss resources before you attempt it again”). This reflects G.U.M.M.I.™ ideals: the AI insights were presented clearly and actionably. Moreover, the system continuously learns; as interventions succeed or fail, the models update to improve future recommendations. 

GSU effectively treated student success as an area for enterprise automation and optimization, applying AI agents to systematically improve an outcome (graduation) that had been stagnant. The result was a flagship example of client transformation in higher education – an institution fundamentally changing how it supports students using data and AI. Importantly, other universities have since followed GSU’s model, showing that such AI-driven personalization is replicable in different contexts with the right leadership and tools.

Bridging AI and Pedagogy: Key Insights and Best Practices

The case studies and examples above highlight that AI can indeed personalize learning and improve outcomes. However, they also reveal how success is achieved – through thoughtful integration of technology with pedagogy. These insights ensure that adopting AI isn’t just a flashy tech upgrade, but a sustainable, positive transformation in teaching and learning.

Center Educators in the Loop: 

Always keep human teachers and decision-makers in control of the AI. When AI is used, it should augment, not replace, the educator’s judgment. Studies of K-12 AI tools have found that teacher acceptance is crucial – educators need to trust the AI and feel it helps them do their job better, not sidelines them. One best practice is to involve teachers in the design and customization of AI systems. For example, allow instructors to adjust difficulty settings or override recommendations. As the U.S. Department of Education recommends, maintain a “human-in-the-loop” approach so that instructional decisions are ultimately overseen by people. This also helps with ethical oversight, ensuring AI suggestions align with educational values and context.

Invest in Training and Change Management: 

Introducing AI-driven systems requires more than installing software; it entails an organizational change in how instruction is delivered. Faculty and staff training is a must. Professional development should focus not just on how to use the dashboard or tool, but on interpreting AI outputs and blending them with pedagogical strategies. In higher ed, for instance, advisors at GSU underwent training to learn how to communicate with students about AI predictions without causing alarm – framing it as “the system suggests you might need extra support in math, and I agree – let’s get you that support,” rather than making the student feel profiled by an algorithm. Additionally, starting with pilot programs and consulting frameworks to gather feedback can smooth the adoption. Many early adopters form an “AI task force” or partner with an AI consulting specialist (like Klover.ai) to develop a roadmap for scaling the solution across the institution.

Ensure Equity and Access: 

One promise of AI personalization is to close achievement gaps, but this only holds if all students have access to the technology and support. Equitable implementation means addressing the digital divide – for example, if a personalized learning app is used for homework, schools must consider students who lack reliable internet or devices at home. Solutions include providing offline capabilities or loaner devices. Moreover, be vigilant about biases in AI models. AI agents should be audited for fairness – ensure that recommendations or risk predictions are not inadvertently discriminating against or lowering expectations for certain groups. Using diverse training data and involving educators in evaluating AI decisions can mitigate bias. In short, personalization should not come at the cost of equity; rather, it should raise the floor for everyone while also raising the ceiling.

Prioritize Data Privacy and Ethics: 

Personalized learning AI systems, by necessity, collect a lot of sensitive data on students – from academic performance to potentially behavioral or biometric data. It is paramount to handle this data ethically and securely. Adhere to student data privacy laws (like FERPA in the U.S.) and be transparent with students about what data is collected and how it’s used. Many institutions create data governance policies when rolling out AI, specifying, for example, that AI analytics are for support, not for punitive measures. As researchers note, data privacy concerns are one of the main challenges cited by educators regarding AI. Build trust by anonymizing data when possible, limiting access on a need-to-know basis, and communicating clearly to students and parents about the benefits and safeguards. An ethical, privacy-by-design approach will ensure the longevity and public acceptance of AI innovations in education.

Iterate and Involve Stakeholders: 

Treat the deployment of AI in education as an iterative process. Gather feedback from students, instructors, and developers continuously. If the analytics dashboard is too confusing, tweak the interface (G.U.M.M.I.™ should evolve with user needs). If the AI tutor’s style isn’t resonating with students, adjust the content or tone. Multi-agent systems can be modular – you might swap out one algorithm for a better one as research advances. Involving stakeholders also means including student voice: students should feel they are partners in this personalized learning journey. Some institutions have student advisory boards for edtech tools, which can provide invaluable insights into how AI is actually impacting learners’ day-to-day experience. The most successful implementations treat AI integration not as a one-off IT project, but as an ongoing collaboration between educators, students, and technologists.

A Vision for Personalized, AI-Augmented Learning

Personalizing learning at scale was once an unattainable dream – an ideal scenario where every student gets the equivalent of a personal tutor and a learning plan tailored just for them. Today, with advances in AI and multi-agent systems, that vision is rapidly coming within reach. By leveraging frameworks like AGD™ for intelligent decision-making, P.O.D.S.™ for real-time adaptive support, and G.U.M.M.I.™ for intuitive human-AI interaction, educators can create learning environments that dynamically adjust to each learner. The result is a classroom (physical or virtual) where every student is appropriately challenged, engaged, and supported – a truly student-centered paradigm powered by AI.

Klover.ai envisions a future in which AI agents are ubiquitous in education, not as a replacement for teachers, but as an empowering force multiplier. Faculty will have at their fingertips an army of data-crunching, pattern-spotting assistants that free them to focus on the human aspects of teaching – mentoring, inspiring, and innovating curriculum. Students will benefit from a personalized journey, with fewer getting lost in the crowd or left behind due to gaps in understanding. And educational institutions as a whole will become more adaptive learning organizations, using decision intelligence to continually refine how they deliver instruction and support learners.

References

Azzam and Charles (2024) provide a comprehensive review of artificial intelligence in K–12 education, highlighting emerging tools and their classroom impact.

According to a report by the Center for Education Policy Research at Harvard, DreamBox Learning implementation in Rocketship and HCPSS schools led to improved learning outcomes.

Georgia State University has seen measurable improvements in student performance through predictive analytics, as reported by Inside Higher Ed.

DreamBox Math from Discovery Education is cited as a dynamic tool for personalized K–8 math instruction.

A study shared by EdSurge reveals that DreamBox Learning significantly improves math test scores, according to Harvard’s research findings.

A recent case study in the Global Educational Studies Review explores the role of artificial intelligence in personalized learning for K–12 students, showing promising results in tailored educational paths.

The U.S. Department of Education’s Office of Educational Technology issued a forward-looking report on AI in education, outlining key insights and policy recommendations for future adoption.

An independent study featured on BusinessWire demonstrates that DreamBox Learning boosts math achievement after just one hour of use per week.

Georgia State University showcases its innovative GPS Advising program as a critical driver of student retention and completion.

Their broader approach to student success provides a model for other institutions aiming to integrate data and advising at scale.