Overview
The online, 30-credit (10 courses) Master in Education (M.Ed.) in Learning Design and Technology (LDT) program equips students with the critical skills and knowledge needed to navigate and advance in the ever-evolving landscape of educational technology. Designed for educators, instructional designers, trainers, and learning leaders, the program integrates theories and ideas from the learning sciences and critical systems thinking to prepare graduates to design, evaluate, and implement technology-enhanced learning experiences.
The LDT program combines practical skill development with a reflective, ethical approach to technology use. Students gain hands-on experience in instructional design, AI and data-driven learning, human-centered design, and technology leadership, ensuring they can apply these skills in K -12, higher education, corporate, and nonprofit settings. At the same time, the program challenges students to critically examine how educational technologies influence learning environments and society, ensuring that innovation serves diverse learners and communities.
With a strong emphasis on real-world application, the coursework includes project-based learning, case studies, simulations, and collaboration with industry and educational partners. Learners may take a three-credit internship with industry or higher education partners for an experiential learning opportunity. Graduates emerge prepared to design inclusive learning experiences, implement emerging technologies responsibly, and lead organizations through technological change.
Program of Study
All LDT students take two foundation courses (ED.893.652: Learning Sciences Studio: Theory, Analysis, and Educational Technology Design and ED.893.654: Critical Perspectives on Educational Technology) in the first two semesters of their academic program. Students take a minimum of two research methods courses and up to two electives (as approved by the student’s program advisor). Students take the three courses in their declared concentration (described below), followed by a capstone course.
Concentrations
Students in the LDT program select one of three concentrations to shape their Required Concentration Courses. Each concentration is designed to serve a distinct professional context and set of career goals. Students should choose the concentration that best aligns with their background, interests, and intended practice area.
Learning Experience Design (LXD)
Learning experience design sits at the intersection of the learning sciences, instructional theory, multimedia design, and data analytics. This concentration prepares students to design, prototype, and evaluate learning experiences grounded in research and responsive to learner variability, and to lead organizational-level design strategy.
| Code | Title | Credits |
|---|---|---|
| ED.893.655 | Applications of Learning Experience Design | 3 |
| ED.893.656 | Advanced User Experience and Interaction Design for Learning Environments | 3 |
| ED.893.658 | Leadership, Strategy, Evaluation, and Program Development in Learning Experience Design | 3 |
Ideal Candidate for LXD
LXD is well-suited to students drawn to the craft of teaching and training who seek to work at a systems level — examining rigorously why some learning experiences succeed, and others do not. Strong candidates typically come from backgrounds in education, instructional design, corporate training, communications, or related fields, and seek to deepen their practice through a rigorous, research-grounded framework. Prior technical experience is not required; however, candidates should demonstrate comfort with analytical thinking and a disposition toward design as an iterative, evidence-driven process.
Curricular Focus
Across the course sequence, students work through iterative design cycles: conducting needs analyses at the course and program level, prototyping solutions, running user research, and evaluating impact. Throughout, the emphasis is on alignment — connecting learning goals, instructional strategies, assessment, and feedback into coherent designs. Students produce tangible artifacts, including instructional modules, technology-supported learning environments, and program-level plans, while building the analytical skills to critique and refine their own work and that of peers.
The culminating course shifts focus from individual learning experiences to organizational leadership, requiring students to develop systems for designing learning experiences that advance measurable goals across an institution.
Career Preparation
Graduates are prepared to contribute to and lead design teams across sectors — including education, business, government, and nonprofit organizations — with the research grounding and practical skills to make design decisions that hold up under scrutiny.
Learning Engineering for Next-Generation Systems (LENS)
In fields such as defense and national security, healthcare, and large-scale education, overall system performance depends on how effectively human expertise is integrated into system design from the outset. LENS prepares students to treat that integration as a design-and-analytics challenge — grounded in the learning sciences, aligned with IEEE learning engineering standards, and calibrated for complex environments where human and system performance are inseparable.
| Code | Title | Credits |
|---|---|---|
| Foundations of Learning Engineering (in development) | 3 | |
| Comprehensive Learning Analytics and Instructional Design (in development) | 3 | |
| Learning Engineering - Integrating Advanced Analytics and Design Solutions (in development) | 3 | |
Ideal Candidates for LENS
LENS is designed for professionals working at the intersection of human performance and complex systems — particularly those whose work carries significant consequences when training and capability development fall short. Strong candidates include learning and performance specialists, human factors practitioners, training systems designers, and program managers in defense, healthcare, government, or large-scale organizational contexts. Candidates should be comfortable engaging with data analytics and measurement, and prepared to apply engineering discipline to problems that are fundamentally about people. Prior experience in operational environments is an asset, though not a requirement.
Curricular Focus
Students develop systems-thinking skills alongside methodological expertise in learning analytics, evaluation, and performance measurement. Coursework covers the full lifecycle of a learning solution: defining capability requirements, designing ethical measurement and data collection, interpreting evidence, and refining designs iteratively. The emphasis throughout is on decisions grounded in rigorous evidence rather than surface-level metrics.
Because learning engineering is inherently interdisciplinary, students develop competencies in cross-functional collaboration — working alongside subject-matter experts, systems engineers, technologists, designers, and organizational leaders to translate operational requirements into effective learning system designs.
Career Preparation
Graduates are prepared to lead large-scale learning initiatives, generate and communicate evidence responsibly, and make design decisions that treat ethics and equitable access to capability development as foundational constraints.
Artificial Intelligence Leadership in Education (AILE)
AI is reshaping how educational institutions operate, but adopting it responsibly requires more than technical fluency — it requires leadership. This concentration prepares students to lead the adoption and governance of AI in educational settings, connecting institutional priorities, ethical frameworks, and organizational change to the realities of how AI systems are designed, evaluated, and deployed.
| Code | Title | Credits |
|---|---|---|
| EN.705.615 | Artificial Intelligence for Leaders | 3 |
| EN.705.608 | Applied Generative AI | 3 |
| Capstone in AI Leadership In Education (in development) | 3 | |
Ideal Candidates for AILE
AILE is designed for current or aspiring educational leaders who are positioned to make consequential decisions about AI and who seek to do so with rigor and informed judgment. Strong candidates typically have three or more years of experience in education or related fields such as corporate learning development, government and military education, healthcare, and clinical education, and are already operating in or moving toward leadership roles. Common backgrounds include school or district administration, higher education leadership, instructional technology and ed-tech implementation, curriculum and academic program management, and education policy or strategy. A technical background is not required; however, candidates should demonstrate genuine interest in understanding how AI systems function, not merely what they produce. Students engaged in evaluating ed-tech vendors, navigating institutional AI policy, or advising faculty and staff on AI adoption will find this concentration directly applicable to their professional responsibilities.
Curricular Focus
Two of these three concentration courses are offered through the Johns Hopkins Whiting School of Engineering, placing students in interdisciplinary courses alongside peers from engineering and related fields. This structure provides direct exposure to foundational AI concepts and contemporary development practices while situating coursework within broader technical and organizational conversations. All students in the AI Leadership concentration are required to complete a 0-credit, 2-week Python with Gen AI online coding bootcamp before enrolling in 705.608: Applied Generative AI.
Across the sequence, students develop practical fluency with contemporary AI approaches — including generative AI — while grounding their work in responsible AI principles. Coursework covers core AI concepts and vocabulary, data quality and bias, privacy and security, risk identification and mitigation, and the broader implications of AI for educational practice. Students carry out hands-on applied work with current development and evaluation patterns, including prompt and workflow design, solution testing and validation, and architectures such as retrieval-augmented generation (RAG) and agentic systems.
Career Preparation
The aim of this concentration is not to produce AI developers. Rather, this technical grounding supports informed leadership: interpreting technical claims, posing substantive questions of vendors and internal teams, establishing appropriate requirements and guardrails, and overseeing implementation in ways that align with educational goals, institutional values, and stakeholder trust.
Program Requirements (30 credits)
In addition to the course requirements below, students will work with their faculty mentor to build a required portfolio that demonstrates their learning throughout the program. While required, the portfolio does not carry course credit. An option to complete a three-credit internship with industry or higher education partners is available.
Visit the School of Education website for more information about the Master of Education in Learning Design and Technology (LDT).
Program Requirements (30 credits)
In addition to the course requirements below, students will work with their faculty mentor to build a required portfolio that demonstrates their learning over the course of the program. While required, the portfolio does not carry course credit. An option to complete a three-credit internship with industry or higher education partners is available.
Please see the additional GPA and Grade Requirements on the Graduation page.
| Code | Title | Credits |
|---|---|---|
| Foundation Courses | ||
Taken sequentially in first two terms of enrollment | ||
| ED.893.652 | Learning Sciences Studio: Theory, Analysis, and Educational Technology Design | 3 |
| ED.893.654 | Critical Perspectives on Educational Technology | 3 |
| Required Specialization Courses - Select one Concentration | 9 | |
| Learning Experience Design (LXD) Concentration | ||
| Applications of Learning Experience Design | ||
| Advanced User Experience and Interaction Design for Learning Environments | ||
| Leadership, Strategy, Evaluation, and Program Development in Learning Experience Design | ||
| Learning Engineering for Next-Generation Systems (LENS) Concentration | ||
Foundations of Learning Engineering | ||
Comprehensive Learning Analytics and Instructional Design | ||
Learning Engineering - Integrating Advanced Analytics and Design Solutions | ||
| Artificial Intelligence Leadership in Education (AILE) Concentration | ||
| Artificial Intelligence for Leaders | ||
| Applied Generative AI | ||
TBD AI Leadership in Education 3 | ||
| Required Research Methods Courses | ||
| ED.830.600 | Introduction to Social Science Research | 3 |
| ED.883.605 | Program Evaluation Methods and Design | 3 |
| Elective Courses 1 | 6 | |
| AI in Education | ||
| Gaming and Simulations for Learning | ||
| Capstone Course | ||
| Capstone in Learning Design and Technology | 3 | |
| Total Credits | 30 | |
- 1
Students must take two elective courses - 6 credits - in consultation with their faculty mentor. Electives may be taken from outside the program and in other JHU divisions. The two electives listed are examples.
Sample Program Plans
FULL TIME
| First Year | ||
|---|---|---|
| Summer Term | Credits | |
| Orientation | ||
| Credits | 0 | |
| Total Credits | 0 | |
| First Year | ||
|---|---|---|
| Fall | Credits | |
| ED.893.652 | Learning Sciences Studio: Theory, Analysis, and Educational Technology Design | 3 |
| ED.830.600 | Introduction to Social Science Research | 3 |
| Elective Course # 1 | 3 | |
| Credits | 9 | |
| Spring | ||
| ED.893.654 | Critical Perspectives on Educational Technology | 3 |
| ED.883.605 | Program Evaluation Methods and Design | 3 |
| Concentration Course # 1 | 3 | |
| Credits | 9 | |
| Summer Term | ||
| Concentration Course # 2 | 3 | |
| Elective Course # 2 | 3 | |
| Credits | 6 | |
| Second Year | ||
| Fall | ||
| Concentration Course # 3 | 3 | |
| Capstone in Learning Design and Technology (in development) | 3 | |
| Credits | 6 | |
| Total Credits | 30 | |
- *
Courses must be taken in the order listed, as each course serves as a prerequisite for the next.
PART TIME
| First Year | ||
|---|---|---|
| Summer Term | Credits | |
| Orientation | ||
| Credits | 0 | |
| Total Credits | 0 | |
| First Year | ||
|---|---|---|
| Fall | Credits | |
| ED.893.652 | Learning Sciences Studio: Theory, Analysis, and Educational Technology Design | 3 |
| ED.830.600 | Introduction to Social Science Research | 3 |
| Credits | 6 | |
| Spring | ||
| ED.893.654 | Critical Perspectives on Educational Technology | 3 |
| Concentration Course # 1 | 3 | |
| Credits | 6 | |
| Summer Term | ||
| Concentration Course # 2 | 3 | |
| Elective Course # 1 | 3 | |
| Credits | 6 | |
| Second Year | ||
| Fall | ||
| Concentration Course # 3 | 3 | |
| ED.883.605 | Program Evaluation Methods and Design | 3 |
| Credits | 6 | |
| Spring | ||
| Elective Course # 2 | 3 | |
| Capstone in Learning Design and Technology (In development) | 3 | |
| Credits | 6 | |
| Total Credits | 30 | |
- *
Courses must be taken in the order listed, as each course serves as a prerequisite for the next
Learning Outcomes
Master of Education (M.Ed.) in Learning Design and Technology (LDT)
1. Theory and Evidence in the Design of Experiences and Solutions
Focusing on the integration of theoretical and empirical evidence from the learning sciences, media, and technology to design effective digital learning experiences and solutions. Learners will be able to:
- Describe and differentiate theories and ideas from the learning sciences, motivation, and media/technology.
- Use a variety of digital technologies to develop learning experiences or solutions for different learning environments and learners.
- Analyze and evaluate design options to identify appropriate and applicable theories and practices to accomplish intended instructional outcomes in a given context and for a learner population.
- Justify design and deployment decisions based on theory, ideas, models, or evidence.
- Design, develop, and deploy digital education solutions grounded in ideas and theories from the learning sciences, motivation, and media/technology.
2. Ethical and Human-Centered Learning Solutions
Emphasizing the creation of learning solutions that are ethical, inclusive, and focused on the diverse needs of learners, adhering to best practices and industry standards. Learners will be able to:
- Identify and distinguish among diverse learner needs and incorporate these considerations into technology-supported learning experiences and solutions.
- Apply systematic models and systems thinking to create ethical, learner-focused digital tools and curricula.
- Evaluate learning solutions to ensure they advance equity and inclusivity across varied contexts.
- Design and develop accessible, inclusive, and flexible technology-supported learning experiences or solutions.
3. Collaboration and Leadership in Educational Technology
Focusing on effective collaboration with stakeholders and leadership in the design, implementation, and evaluation of educational technology solutions. Learners will be able to:
- Collaborate with various involved and interested parties in a constructive, professional, and respectful way.
- Communicate shared visions for technology-supported learning experiences.
- Lead efforts to ensure the quality of technology-supported learning experiences.
- Lead ongoing evaluation efforts to continuously improve the selection and implementation of technologies and technology-based solutions.
- Collaborate and lead to develop evidence-based solutions to address instructional and/or programmatic needs using a variety of appropriate educational technologies and techniques.
4. Communication Competence
Communicating design and pedagogical decisions effectively using various media and tailored to diverse audiences. Learners will be able to:
- Craft effective messages using appropriate media in written, verbal, and visual modalities.
- Articulate and justify pedagogical, design, and development decisions to diverse audiences.
- Communicate effectively with all invested parties to facilitate the design and development of learning products and solutions.
5. Sociocultural Aspects of Educational Technology
Addressing the analysis of sociocultural factors, including power dynamics and privilege, and developing solutions to mitigate inequities in the design and deployment of educational technology and technology-supported solutions. Learners will be able to:
- Analyze how socioeconomic systems influence technology adoption and evaluation.
- Evaluate power dynamics and privilege affecting educational technology designs and implementations.
- Develop solutions to address and prevent injustices through educational technology designs and implementations.
- Design and develop equitable, inclusive, and accessible technology-supported systems, processes, and products.
6. Data-Informed Learning Design, Implementation, and Evaluation
Using data to inform the design, development, and evaluation of learning experiences and technology-supported solutions, ensuring continuous improvement through evidence-based decision-making. Learners will be able to:
- Collect data using a variety of evaluation and research methods to gain insights into learner and educator needs and tools effectiveness.
- Use data to revise designs, practices, and goals based on evaluation data for continuous improvement.
- Analyze educational data using qualitative, quantitative, and mixed methods.
- Design and implement comprehensive data collection and analysis efforts to inform decisions concerning design, development, delivery, and evaluation of programs, processes, and/or products.