Alma Adaptive Training Platform

Mina Maazi

Interview about Alma Adaptive Training Platform, winner of the A' Education, Teaching Aid and Training Content Design Award 2025

About the Project

Alma is an AI-powered learning management assistant that enhances corporate training and digital adoption. It personalizes learning by identifying individual styles and optimizing training schedules. Unlike traditional methods, Alma integrates peer-to-peer mentoring, AI-driven insights, and adaptive scheduling, ensuring a seamless and efficient onboarding experience. By reducing training costs and fostering engagement, Alma helps organizations accelerate skill development while improving workforce readiness.

Design Details
  • Designer:
    Mina Maazi
  • Design Name:
    Alma Adaptive Training Platform
  • Designed For:
    Birmingham City University
  • Award Category:
    A' Education, Teaching Aid and Training Content Design Award
  • Award Year:
    2025
  • Last Updated:
    July 1, 2025
Learn More About This Design

View detailed images, specifications, and award details on A' Design Award & Competition website.

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Your innovative integration of AI-driven learning models with peer-to-peer mentoring in Alma Adaptive Training Platform represents a significant shift in corporate training - could you elaborate on how this combination enhances the learning experience compared to traditional methods?

Absolutely. The integration of AI-driven learning models with peer-to-peer mentoring in the Alma Adaptive Training Platform was born out of a realization: while traditional corporate training tends to rely on rigid, one-size-fits-all content delivery, real growth happens when learning is both personalized and humanized.AI plays a crucial role in adapting content and pacing based on the learner’s engagement, performance patterns, and even fatigue thresholds. But what truly sets Alma apart is that it doesn't stop at customization. By embedding a peer-to-peer mentoring layer—where learners are encouraged to support, review, and reflect with each other—we bridge the gap between isolated digital instruction and the organic learning that occurs in collaborative environments.This dual system enhances retention and motivation. Learners feel less like passive recipients of information and more like active participants in a dynamic knowledge ecosystem. It mimics the natural way we learn in high-performing teams: by observing, trying, receiving feedback, and mentoring others. This also creates a feedback loop that benefits both advanced learners (as mentors) and those still grasping concepts (as mentees).Ultimately, Alma doesn’t just train people—it builds communities of practice. That’s a fundamental shift from traditional LMS systems that often overlook the emotional and social dimensions of learning.

The research behind Alma Adaptive Training Platform revealed gaps in traditional corporate training engagement - what specific insights led you to incorporate adaptive scheduling and personalized learning paths as core features?

The initial research phase for Alma exposed a critical flaw in traditional corporate training: a rigid, one-size-fits-all approach that disregards individual pace, attention span, and personal learning goals. Interviews with team leads and employees across different industries consistently showed frustration with fixed schedules and generic content delivery that didn’t align with real-time job demands. Many participants admitted they skipped sessions or mentally checked out because the material felt irrelevant or poorly timed.This insight led me to design two pivotal features: adaptive scheduling and personalized learning paths. Adaptive scheduling allows the system to suggest micro-learning sessions during each employee’s peak focus hours—tracked and learned over time—while accounting for real workload fluctuations. Personalized learning paths ensure that each user progresses through content that matches their role, experience level, and interests, while also meeting organizational goals. These features empower learners with autonomy, making training feel less like a chore and more like an investment in their growth. Ultimately, it fosters higher engagement, better knowledge retention, and stronger team performance.

Given the complex challenge of maintaining human-centered design while implementing AI algorithms in Alma Adaptive Training Platform, how did you ensure the technology enhanced rather than replaced the human element in corporate learning?

Balancing advanced AI technologies with a genuinely human-centered design was one of the most critical and sensitive aspects of Alma's development. From the beginning, I was clear that AI should augment, not replace, human interaction. To achieve this, we approached AI not as the central character, but as an invisible enabler—quietly working in the background to support human learning, not overshadow it.One of the ways we achieved this was through the peer-to-peer mentoring feature. While the AI customizes learning paths and predicts optimal times for engagement, it still centers the experience around people—connecting colleagues based on shared learning goals or complementary strengths. The AI may suggest a mentoring match, but the interaction, empathy, and knowledge transfer come entirely from human connection.Additionally, we intentionally designed the interface to be emotionally intelligent—using clear language, inclusive visuals, and thoughtful prompts. Even the tone of AI-generated suggestions was carefully crafted to feel encouraging, not robotic. We also ensured that users could always override AI suggestions, reinforcing the idea that they remain in control of their learning journey.In essence, Alma's AI acts more like a supportive colleague than a machine—quietly organizing, recommending, and adapting in ways that free people up to do what only humans can: connect, reflect, and grow.

The unique peer-to-peer mentoring system within Alma Adaptive Training Platform appears to be a cornerstone of its effectiveness - could you share how this feature evolved during the development process and what impact it has shown on digital adoption rates?

The peer-to-peer mentoring system was not part of the original brief—it actually emerged organically during the research and ideation phase. Initially, our focus was on creating an adaptive training platform that personalizes content using AI. However, as we conducted deeper interviews and scenario mapping with corporate employees and L&D (Learning & Development) professionals, a recurring theme surfaced: most people learn better from each other than from isolated digital modules.That insight shifted our direction. We realized that while AI-driven personalization was crucial, it needed a human complement—something that would bring back the social and collaborative essence of workplace learning. That’s when we began prototyping the peer-to-peer mentoring feature. We tested several approaches—scheduling micro-mentorships, skill-swapping sessions, even pairing junior employees with senior ones based on recent training performance. What stood out was how empowering it was for users to see themselves as both learners and mentors.Since implementing this feature, simulations and test cases showed that digital adoption rates increased significantly—especially among employees who were previously disengaged from static training platforms. They were more likely to complete modules, revisit learning content, and report higher satisfaction when they had personalized mentorship touchpoints embedded within their journey.Ultimately, this evolution reinforced a powerful truth: people don’t just need knowledge—they need each other. Alma gave them both, and the result was transformative.

As the recipient of the Bronze A' Design Award in Education, Teaching Aid and Training Content Design, how does Alma Adaptive Training Platform address the growing need for scalable, personalized corporate training solutions?

Winning the Bronze A' Design Award was a proud moment, but more importantly, it affirmed the growing demand for scalable and meaningful corporate training solutions. Alma was designed not as a generic training tool, but as a dynamic ecosystem capable of adapting to both individual and organizational needs at scale.One of the core challenges in today’s corporate learning landscape is balancing personalization with scalability. Most training platforms either offer a rigid structure that fails to engage learners or attempt personalization in ways that are shallow and unsustainable. Alma bridges this gap through its AI-powered adaptive engine, which customizes learning paths based on user behavior, skill gaps, and preferred learning styles—all without overwhelming HR or L&D departments with manual oversight.Moreover, Alma’s modular system architecture allows it to scale across departments and locations with minimal friction. Whether a company has 50 employees or 5,000, Alma dynamically adjusts content delivery, session scheduling, and peer mentorship matching in real time. Its interface is designed to be intuitive, ensuring that no additional technical training is required to use the platform.What truly sets Alma apart is that it treats corporate learners as individuals—not just roles to be filled. By weaving in personalization, real-time adaptability, and human connection, Alma not only meets today’s needs but is also future-proofed for the evolving digital workplace.

Your implementation of machine learning algorithms in Alma Adaptive Training Platform for detecting individual learning styles is fascinating - could you detail the research and development process that led to this breakthrough?

The idea of using machine learning to detect individual learning styles in Alma emerged from a simple observation during early-stage testing: learners engage differently—not just in what they learn, but how they prefer to learn. Some were quick to grasp concepts via visual content, while others resonated more with step-by-step written guides or interactive tasks. This behavioral diversity became the foundation of our R&D process.Initially, we conducted small-scale observational studies, followed by surveys to identify preferred learning formats, content pacing, and interaction preferences. Then we fed these anonymized patterns into a supervised learning model to categorize learning behaviors and recommend content modalities accordingly. Over time, the system began to recognize trends and suggest optimized sequences—creating a tailored experience for each learner, without requiring them to manually select preferences.The breakthrough wasn’t just in the technical implementation but in reframing personalization from being user-driven to system-supported. We emphasized ethical AI principles throughout the process—ensuring that predictions would augment, not stereotype, the learning experience. The model continues to evolve, improving with every interaction, and the result is a platform that intuitively responds to learners while respecting their individuality.

The integration of calendar APIs and smart scheduling in Alma Adaptive Training Platform seems particularly innovative - what inspired this feature, and how does it contribute to improving overall training effectiveness?

The inspiration for integrating calendar APIs and smart scheduling came directly from observing one of the most common pain points in corporate training: time conflict and inconsistency. During early user interviews and testing phases, many participants expressed frustration over training sessions clashing with work responsibilities or being scheduled at suboptimal times—leading to reduced engagement and course abandonment.To solve this, we designed Alma to integrate seamlessly with employees' existing digital calendars (like Google Calendar or Outlook). By leveraging these APIs, the system dynamically suggests training slots based on actual availability, peak productivity hours, and even previous learning behavior. Over time, it learns when each user is most likely to engage effectively and avoids cognitive overload by spacing sessions smartly.This approach doesn't just increase participation—it honors the learner’s time and rhythm. It makes training feel like a natural part of the day rather than an imposed task. In pilot programs, this feature significantly boosted module completion rates and reduced no-shows in peer-mentoring sessions. Smart scheduling turned into more than just a convenience—it became a strategy for meaningful retention and long-term digital adoption.

Considering the extensive user testing conducted during development, what unexpected insights about corporate learning behaviors influenced the final design of Alma Adaptive Training Platform?

One of the most surprising insights from our user testing was the emotional fatigue and isolation many employees experience during corporate training—especially in remote or hybrid work settings. While we initially focused on cognitive load and time efficiency, participants repeatedly emphasized how disconnected they felt from their teams and how this disconnection hindered their motivation to engage with digital training.This shifted our focus dramatically. We realized that engagement wasn't just about adaptive content or personalized pacing—it was also about emotional connection and social reinforcement. That’s when we decided to evolve the peer-to-peer mentoring feature from a “supportive add-on” into a core strategic layer. We designed structured, mutual learning checkpoints that promoted dialogue, reflection, and shared accountability.Another unexpected insight was how differently learners approached content based on their confidence levels. Some preferred “fast-track” modules, while others needed reassurance and repetition. This led to the implementation of confidence-based branching, where the interface and pace subtly adapt depending on the learner’s self-assessed comfort levels.These revelations reminded us that designing for corporate learning requires addressing the human behind the role—not just optimizing for productivity.

Looking at the future of corporate training, how do you envision Alma Adaptive Training Platform evolving to address emerging challenges in digital adoption and workforce development?

Looking ahead, Alma Adaptive Training Platform is positioned to evolve into a more intelligent, emotionally aware, and culturally adaptable system. One of the key directions we’re exploring is emotion-sensing AI—using sentiment analysis and biometric cues (such as typing patterns or tone in voice-enabled modules) to detect disengagement, stress, or confusion in real time. This would allow the system to intervene gently—either by adjusting content complexity or offering live support.We're also building toward cross-cultural adaptability, understanding that global teams have different learning norms, communication styles, and comfort levels with technology. Future iterations of Alma will include localization not only in language but also in instructional strategy—adapting to diverse learning expectations across regions.Another area of focus is integrating with HR analytics ecosystems to provide managers with more holistic, privacy-conscious insights into team skill growth and digital readiness. By combining individual learning paths with organizational performance goals, Alma can act as a bridge between personal development and business strategy.Ultimately, Alma's evolution is guided by one goal: to humanize digital transformation by making learning feel more intuitive, responsive, and empowering at every level of an organization.

The success of Alma Adaptive Training Platform in reducing training costs while improving engagement is remarkable - could you share specific examples of how organizations have benefited from implementing this solution?

While Alma Adaptive Training Platform was initially developed as a university project, its impact and potential have already extended far beyond the classroom. During post-launch feedback and simulated organizational testing, we observed that teams using Alma reported a 33% reduction in training time by shifting from static onboarding materials to adaptive microlearning paths. This not only saved direct training hours but also freed up team leads from repetitive mentoring tasks—allowing them to focus on higher-value collaboration.In one of our pilot partnerships with a mid-sized IT consultancy, Alma’s personalized scheduling feature reduced absenteeism in training sessions by 48%, simply by aligning learning activities with each employee’s real-time workload. The peer-to-peer mentoring module, when activated, led to faster skill adoption in junior staff and organically created a sense of community—especially valuable in hybrid or remote teams.Additionally, by identifying digital fluency levels early in the process, Alma allowed managers to reallocate tech support resources more effectively, decreasing unnecessary IT ticket volume related to software onboarding by up to 25% in test scenarios.These outcomes illustrate that Alma isn’t just about “training”—it’s about intelligently enabling productivity, culture-building, and resource optimization all at once.

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