The Emergence of Learning Engineering

Table of contents

  1. Defining Learning Engineering 
  2. The learning Engineer: Redefining Educational Development 
  3. Learning Engineering and Instructional Design: Collaborative Dynamics
  4. The Purpose of Learning Engineering
  5. The Learning Engineering Process
  6. Integrating Learning Engineering into Product Development 
  7. Integrating Learning Science Consultation into Product Development 

I First learnt about learning engineering through my participation in the Tools Competition. Since then, I have further explored the field. In 1972, Herbert Simon, introduced the concept of a “learning engineer” in his influential article, “The Job of a College President.” Simon emphasized the pivotal role these professionals would play in collaborating with faculty members to foster student engagement and create transformative learning experiences specific to disciplines, aiming to demonstrate tangible enhancements in learning efficacy and effectiveness, starting with modest-scale implementations. (Simon, 1972)

Simon envisioned colleges as hubs for a breeding ground for a rich pool of knowledge and skills, advocating for proficient administrators, presidents, deans, department heads, and faculty members who possess mastery in effective management, teaching and learning, all geared towards creating a holistic learning environment. Delving deeper into the science of learning, I dedicated considerable time to reading research papers and resources, driven by my aspiration to become an Edtech founder. Simultaneously, my participating in the Tools competition fueled my drive, motivated by the prospect of securing grant funding. My primary goal is to use learning engineering techniques and principles to develop innovative solutions, in an impactful and effective manner. 

I believe that application of learning engineering to edtech development yields profound insights derived from the integration of learning science and computer science. It emphasizes the intentional application of sound learning principles to significantly elevate learning outcomes and facilitate the implementation of educational technology, effecting transformative shifts in the educational landscape.

This fusion provides invaluable guidance for the meticulous design and development of Edtech solutions. It underscores the importance of leveraging the principles of learning science to create impactful educational technologies, ultimately shaping my approach to innovation in education technology solutions at  Studomia . I could say that the essence of learning engineering is in its ability to harmoniously blend the domains of learning and computer science, facilitating thoughtful design, seamless implementation and strategic investment in learning technologies that serve learners and the broader community. 

Defining Learning Engineering

Learning Engineering represents an emerging discipline at the intersection of learning science and computer science that seeks to design learning systems with the instrumentation, data, and Partnerships in the research community. This approach facilitates the creation of tight feedback loops and continuous enhancements in the delivery of learning in online and blended settings. It involves employing human-centered engineering design methodologies and data-informed decision-making to support learners and their development. The primary objective is to leverage data for the continuous improvement of learning and teaching methods.

Advancements in learning analytics, big data utilization, cloud technologies, and personalized learning has paved the way for adoption of learning engineering practices in diverse ways. Learning Engineering also draws from various disciplines such as learning design disciplines (UXD, ID/ISD, Cognitive/ED psych, learning sciences) other parts from science and engineering (system engineering, feedback loops, computer science, data sciences) and psychometrics (field of study concerned with the measurement of knowledge, abilities, attitudes, and personality traits). 

Learning engineering, Goodell et al (2020)

The Learning Engineer: Redefining Educational Development

A learning engineer, as defined by Bror Saxberg, the Vice President of Learning Science at the Chan Zuckerberg Initiative, is an individual who draws on evidence-based information about human development, including learning, and endeavors to apply these findings at scale, within specific contexts, to create affordable, reliable, and data-rich learning environments. Within a team, the “learning engineer” acts as the linchpin, bridging the multidisciplinary aspects of design and development.

The role of a learning engineer revolves around the adept use of technologies, standards, and scientific principles to propose, test, and implement solutions. These professionals possess in-depth knowledge of the principles of learning and the techniques for their practical application. The collaborative efforts of learning engineers are complemented by the contributions of learning scientists, instructional designers, software engineers, and data scientists, all working together to advance the field of learning engineering.

In the capacity of a consultant, a learning engineer assists educators in the development of courses, particularly in online or blended modalities. Working as one-on-one consultants, they provide guidance to program leaders and institutions on overall learning program design and policies. This consulting model can be scaled to encompass a multidisciplinary team supporting more than 25 educators.

Key to the role of a learning engineer are foundational competencies that extend beyond specialized expertise, encompassing diverse fields such as data science, design, software, subject matter expertise, pedagogy, and psychometrics.

Learning Engineering and Instructional Design: Collaborative Dynamics

In the educational landscape, learning engineers often engage closely with administrators, spearheading initiatives at the program level. In contrast, instructional designers collaborate directly with instructors, focusing on the conceptualization and restructuring of individual courses.

The distinct roles of learning engineers and instructional designers manifest in their approaches to decision-making. Learning engineers delve into research on learning and data science, utilizing qualitative analysis to inform their strategic choices. On the other hand, instructional designers rely on qualitative judgments, catering to individualized needs and, in some instances, drawing upon existing research in the field of learning science.

While learning engineers contribute to the broader developmental framework of educational programs, instructional designers concentrate on the intricacies of course design, ensuring that each learning experience is tailored to meet specific learning objectives and student requirements.

The Purpose of Learning Engineers

Learning engineers are dedicated to pursuing projects that delve into the realms of AI and adaptive learning technology, xAPI and learning analytics, competency frameworks, learning data standards, and learning data governance. They are at the forefront of shaping learning experiences (LX) design, actively seeking to utilize interaction data gathered from immersive learning experiences.

Their primary objective is to construct a comprehensive profile of the learner, providing valuable insights that can be used to enhance and personalize the learning experience for each individual. By exploring how learning and education can be effectively supported, learning engineers contribute to the continuous evolution of the field, both within the industry and academia.

Furthermore, they play a significant role in supporting the development of open-source repositories, libraries, and test-beds, fostering an open approach crucial to the ongoing advancement of the learning engineering discipline.

The learning Engineering Process

The learning engineering process commences by identifying a specific problem or challenge associated with learners and their learning experiences. Often intricate and multifaceted, these issues necessitate decision-making that spans across various levels of the contextual system.

Selecting a multidisciplinary team capable of tackling the complexities of the problem is the next crucial step. The composition of this team is tailored to align with the nature of the problem at hand, ensuring the inclusion of the necessary expertise and skill sets.

The iterative process of design begins, focusing on the development of a solution alongside the engineering of appropriate instrumentation, encompassing data definitions, measurement methods, instruments, and technologies. This iterative design process involves multiple rounds of refinement, with a strong emphasis on incorporating insights from data analysis.

Upon implementing the designed solution, data collection through the established instrumentation takes place. Subsequently, a comprehensive data analysis is conducted to explore the outcomes. The results of this analysis often reveal gaps in the solution’s efficacy in fully addressing the identified problem, leading to the formulation of further questions derived from the data. This iterative cycle may involve revisiting design decisions or necessitate additional implementations to effectively bridge these gaps.

Addressing these gaps can potentially lead to the emergence of new challenges or become part of the iterative process, fostering continuous refinement and improvement within the learning engineering framework.

Integrating Learning Engineering into Product Development

As learning-focused organizations, we must embody a culture of continuous learning and adaptability, remaining receptive to ongoing changes in our field. Demonstrating our commitment to learning necessitates actively seeking feedback, implementing necessary changes, and consistently iterating on our products.

Embracing the role of observers of patterns and agents of change, we recognize the importance of responding to these changes by introducing new products and features that effectively cater to the evolving needs of our students.

Integrating learning engineering into the product development process requires a cohesive alignment between the dedicated product team and experts in the field of learning science. This alignment is fostered through:

  • Establishing shared common goals and educational outcomes.
  • Cultivating a clear product vision and maintaining a consistent product roadmap.
  • Encouraging collaboration and establishing robust feedback loops.
  • Emphasizing the evaluation and validation of results to drive continuous improvement.

A clear product vision can encompass specific learning outcomes for students, overarching goals for classrooms and teachers, an increase in the adoption rate of particular subjects, improvements in student confidence, equitable instruction and lesson content, and the provision of essential skills for students’ future success.

The development process necessitates a consistent roadmap that guides both teams through the various stages. Identifying agreed-upon educational outcomes serves as a guiding star against which teams can measure the progress of a product or its new features. This approach accelerates the ability to align, build, and iterate upon a product, ensuring that it remains adaptive and responsive to the dynamic landscape of learning.

Integrating Learning Science Consultation into Product Development

To ensure the integration of learning science principles into the product development process, the product team should proactively incorporate opportunities for consultation and feedback from learning science experts at key stages of development.

During the initial stages of discovery and exploration, engaging with learning science experts can offer valuable insights for clarifying the product vision and establishing a clear focus for development efforts.

In the design phase, consistent communication with learning scientists is instrumental in enhancing the intentionality behind design choices, ensuring alignment with the desired learning outcomes and impact.

During the validation and testing phases, collaboration between the learning science and efficacy research teams becomes crucial. This collaboration enables the alignment on key metrics of interest and facilitates the identification of specific aspects of the product that could be modified or iterated upon based on the results of comprehensive studies.

Establishing a systematic approach for validating learning science-informed features is vital. Once a product or feature has been developed with the intent of influencing specific learning outcomes or principles, teams must engage in continuous experimentation and product iteration over an extended period. This process involves validating the efficacy and capabilities of the tool through research and long-term studies, ensuring its effectiveness in real-world learning environments.

Moreover, fostering an environment receptive to feedback from customers is essential. Acknowledging the expertise of teachers and end-users as field scientists and partners in the learning process is key to ensuring that the product effectively addresses the needs of learners.

Recognizing the pivotal role of educational technology in addressing the challenges faced by educators, especially in the context of addressing learning loss resulting from the pandemic, it becomes imperative to test the impacts of learning science product integrations on intended outcomes through comprehensive efficacy research. Such an approach enables organizations to establish a robust system for rapid testing and iterative enhancement, ensuring the continued evolution and effectiveness of their products.


  • Simon, H. (1972). The Job of a College President.

About Author

Hello, I’m Adesunloye Adeola, the Edtech Founder of Studomia, passionate about solving real-world challenges in education and Edtech. With a strong background in Venture Capital, Computer Science, and Educational Science, I’m committed to making Edtech more accessible and effective.

My expertise spans Learning Engineering, Research, Product Management, Marketing and UI/UI Design, forming the foundation of Studomia’s innovative solutions. Join me in revolutionizing the future of education.

Follow my journey and connect with me on LinkedIn. 

LinkedIn:  Adesunloye (Tennerick) Adeola

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