Resources for Professionals Interested in Developing Learning Technologies

Learning technologies emerged with oral communications around the campfire and stones to carve cave walls. Over time, learning tools have evolved dramatically, from the Sumerian clay tablets of 3200 BCE to today’s AI-powered interactive presentations and immersive virtual field trips.

Fueling these advancements: the field of learning technology. Practitioners in this field focus on analyzing, designing, developing, and maintaining everything from tools to platforms to systems and networks that support learning, teaching, and training today.

On this resource page you’ll find answers to the following key questions:

• Which learning technologies are foundational? Master fundamentals such as AI-based tools, personalized learning, and immersive technologies.
• Which trends are shaping learning technology’s future? Experimental AI is out, AI integration into infrastructure is in (as are VR, AR, and XR, used intentionally).
• What challenges does the field face? Urgent issues include data security and academic integrity in the AI age.
• What are some promising career paths in learning technologies? AI skills are becoming requisite; key jobs include education learning technologist and LMS administrator.
• Which ethical challenges are most urgent? Equity, access, and privacy policies top the list.
• How can I stay up-to-date on learning technology news and research? Access the latest standards, SME insights, and industry trends.

Learn more about the field at the IEEE International Conference on Advanced Learning Technologies

Learning technologies are becoming increasingly common throughout formal education, from pre- and grade school classrooms to doctoral seminars. They’re also routinely used in organizational learning and training programs across public and private sector companies and institutions.

Learning technologies include diverse tools and systems:

• Learning analytics and AI-driven tools
• Adaptive and personalized learning systems
• Mobile and online learning platforms
• Game-based and immersive environments
• Digital pedagogy and instructional design tools

Regardless of the domain, system, or tool, the overall goal of the learning technology field has remained consistent across time: to use existing and emerging technologies to improve learning, teaching, and educational outcomes.

“Artificial Intelligence in education, especially generative and adaptive learning systems, is the single most transformative emerging technology in education, learning, and training today. Its ability to personalize learning at scale, provide real-time feedback, and simulate immersive experiences will definitely have the greatest impact over the next decade.” –Dr. Anil Chaudhary, Professor, IT Department, Swami Keshvanand Institute of Technology (SKIT), Jaipur

According to eLearning Industry, the top trend for 2026 will be AI-driven learning technologies moving from experimental, ad-hoc use to being an embedded part of the core learning infrastructure.

AI is expected to drive key learning areas from academic advising and tutoring to platforms that generate content and offer real-time feedback on student work. AI will also fuel collection and analysis of engagement and performance data to provide individualized learning paths and identify at-risk students.

Learn more:

• AI Infrastructure in K-12 Education
• AI Implementation Is Essential Education Infrastructure
• Integrating Artificial Intelligence in Higher Education
• AI Infrastructure Aims to Lower Barriers—and Raise Standards—for K-12 Vendors

Static learning plans are giving way to AI-based learning platforms that track a student’s behavior, preferences, and patterns to create adaptable, user-tailored learning programs.

Researchers are also exploring beyond personalized learning to create precision learning technologies shaped not just by what users know and do in the platform, but also by their broader interests, goals, and psychological characteristics.

Learn more:

• Precision Learning: Unlocking the Next Transformation in Higher Education
• How Does Technology Cater to Student Needs? A 2026 Perspective
• Personalization in 2026: Designing Logic Across the Learning Cycle

Mixed-reality learning technologies can increase student engagement, curiosity, and motivation, as well as facilitate skills acquisition in training situations. Mixed-reality learning technologies are particularly important when spatial understanding is essential or when costs or safety requires the use of simulations.

However, as with AI, virtual, augmented, and extended realities (VR/AR/XR) are being implemented in more surgical ways today compared to their previous roles as novelty technologies. Also like AI, organizations are increasingly requiring that a solid business case exists before committing funds to immersive technologies.

From the U.S. to Europe to nations across the world, budgets for education are tightening and uncertainties loom over how to use advanced technologies in an effective, measurable way. Unlike in previous years, however, solving the measurability problem now could be the difference between a tech investment and a hard pass.

Learn more:

• Establishing ROI for EdTech Tools
• Calculating EdTech ROI Without a Data Scientist
• Measuring ROI of Educational Technology

“AI-driven rapid change and unequal access to learning content are the two biggest, immediate challenges for learning technologies: AI in the education ecosystem creates rapid opportunities and also amplifies bias, privacy, and personalized-skills-mismatch risks … while AI infrastructure and funding gaps limit equitable benefits.” –Dr. Anil Chaudhary, SKIT, Jaipur

As the Christensen Institute points out, many of today’s learning technology issues and challenges are ongoing in education today, including

• Scarce funding
• Access and attainment gaps
• The isolation tradeoff of technology’s personalization power and efficiency gains

In workplace education and training, eLearning Industry highlights challenges such as

• Improving just-in-time learning
• Creating context-aware microlearning
• Using AI responsibly to improve learning design

In addition to these issues, emerging challenges include the following.

According to the Center for Internet Security, schools are among the most vulnerable and targeted public sector institutions. This vulnerability has many causes, including

• Increasingly advanced AI-generated attacks
• Fragmented institutional infrastructure
• Legacy systems
• Weak links in a complicated vendor infrastructure

Beyond schools, the rapid proliferation of learning technologies across societies and organizations shows no signs of slowing. Protecting the massive amount of sensitive data they generate, collect, and store is a major security and privacy concern.

Learn more:

• Why Cybersecurity Risk Management Is Critical for Schools
• How AI is Transforming Training Across Industries
• Robust Cybersecurity Is Now Essential in Education

Integrating AI into daily education and training raises myriad issues; perhaps none are more pressing and challenging than

• Ensuring academic integrity. Today, students can easily use generative AI to write their assigned essays and papers. While technological AI-use checks are common, they’re also unreliable; this wide margin of error can lead to false accusations and damage trust all around.
• Protecting learning and comprehension. As Harvard’s Dan Levy points out, learning occurs when the brain actively engages in making sense of a topic, not when students simply ask a chatbot to answer a question.

Using AI to do “grunt work,” such as finding reputable sources and explaining basic concepts can be hugely helpful. Using AI to do the actual work, from writing essays to solving problems, risks flat-lining critical thinking and a loss of knowledge acquisition.

Indeed, in the TalentLMS Annual L&D Benchmark Report, 36% of employees said that using GenAI tools was “weakening their ability to solve problems on their own.”

Learn more

• Teaching Academic Integrity in the Era of AI
• The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects in Knowledge Workers
• AI and Academic Integrity: Exploring Student Perceptions and Implications for Higher Education

Technologies offer great promise for helping employees excel in the workplace and students succeed in classrooms.

However, as the Brookings Institute points out, ensuring that workplace training funds are reaching the sectors and people who need them most is becoming an even bigger issue as AI adoption threatens to disrupt employment across industries.

In schools, the situation is even more challenging, as fulfilling technology’s promise depends on adequate infrastructure, functioning hardware, and, of course, funds to buy and support them. All of these factors are in short supply in many schools in poor and/or rural areas in both low- and high-income nations.

• Five Benefits of Corporate Employee Training and Development
• Workforce Development Policy in the U.S.
• Challenges and Opportunities for Education in the Global South
• Bridging EdTech gaps: Examining Learning Equity in Low-Income Settings
• EdTech Accessibility: Supporting Students with Disabilities

“AI, data literacy, and instructional and pedagogical design are the most essential hard skills for a learning technology profession today and must be paired with strong soft skills like communication, facilitation, and change management, which should focus first on AI, education metadata data, and learning engineering, then add UI/UX, analytics, and IT education in engineering platforms to be job-ready in a competitive job market… and, as we all are aware, tech hiring trends highlight AI, cloud, and analytics as top priorities for 2026 up-skill.” –Dr. Anil Chaudhary, SKIT, Jaipur

According to Precedence Research, the global education market for AI is expected to be USD$9.58 billion in 2026, and Grand View Research predicts that the learning technology market as a whole will reach USD$348.41 billion by 2030.

Rapid growth bodes well for job seekers, particularly those with the right skills. As EdTech Jobs notes, AI literacy is becoming baseline in the learning technologies job market. Key jobs in this sector include the following.

• Focus: Identifying or designing technology solutions to specific problems, and integrating, testing, and supporting those solutions.
• Sectors: Education, libraries, public and private sector organizations.
• Top job titles: Learning technologist, instruction technologist, technical program manager, learning and development technologist.
• Requirements: A bachelor’s degree in instructional technology, education technology, or information systems + 1–3+ years’ experience with learning technology development and support.
• Key credentials: CETL certification
• Salary range: USD$60,000–$148,000+
• Where to network: The IEEE International Conference on Advanced Learning Technologies (ICALT)
• Related research: IEEE Transactions on Learning Technologies

• Focus: Implementing and using instructional software and educational technologies; improving network infrastructures and systems configurations; and managing system maintenance and upgrades.
• Sectors: Education, public and private organizations.
• Top job titles: Technology integration specialist, EdTech integration specialist.
• Requirements: Bachelor or master’s degree in computer science, information technology, and/or education + a focus on or experience in curriculum development, learning support, and digital learning.
• Salary range: USD$44,900–88,500+
• Where to network: The IEEE International Conference on Advanced Learning Technologies (ICALT)
• Related research: IEEE Transactions on Learning Technologies

• Focus: Managing day-to-day LMS operations and performance, as well as designing learning paths, configuring courses, and collaborating with team members and users to enhance learning.
• Sectors: Education, public and private sector organizations.
• Top job titles: LMS administrator, learning management system administrator, learning and development instructional technologist (LMS), LMS technical administrator.
• Requirements: Bachelor or master’s degree in information technology, instructional design, or a related field + LMS experience as an administrator or in a support role.
• Key credentials: Certifications in specific systems, such as SAP or Canvas.
• Salary Range: USD$63,300–88,500+
• Where to network: The IEEE International Conference on Advanced Learning Technologies (ICALT)
• Related research: IEEE Transactions on Learning Technologies

• Focus: Managing and integrating learning technology projects across a school, school district, or organization, including establishing project timelines and engaging in technology research, selection, and maintenance.
• Sectors: Education, public and private sector organizations.
• Top job titles: Learning technology coordinator, education technology coordinator, community technology coordinator.
• Requirements: Bachelor or master’s degree in education, instructional technology, education technology, or information systems + 3–5 years’ experience in teaching, lesson planning, or curriculum development.
• Key credentials: ISTE Educator Certification Program
• Salary Range: USD$44,000–78,000+
• Where to network: The IEEE International Conference on Advanced Learning Technologies (ICALT)
• Related research: IEEE Transactions on Learning Technologies

• Focus: Working with department leaders and IT teams to coordinate selection of digital learning technologies and assessment tools. This role also oversees faculty training and performance, as well as development of technology policies and procedures.
• Requirements: Bachelor or master’s degree in IT or a related field + 2–5 years’ experience in learning technology management or support in a school, school district, university, or organization.
• Sectors: Education, public and private sector organizations.
• Key credentials: CETL certification
• Salary Range: USD$111,000–177,000+
• Where to network: The IEEE International Conference on Advanced Learning Technologies (ICALT)
• Related research: IEEE Transactions on Learning Technologies

“AI bias, data privacy, and unequal access are the most urgent ethical issues in education and learning technologies today; addressing them requires immediate IT-empowered governance, transparency, and low-bandwidth design for developing and developed countries.” –Dr. Anil Chaudhary, SKIT, Jaipur

As the World Economic Forum notes, AI’s potential to dramatically improve global training and educational outcomes necessitates a proactive approach to harnessing these technologies while carefully addressing the practical and ethical challenges they pose. Among the latter are the following.

As AI technologies continue their infiltration into academic and organizational learning technology, data regulations and policies at the national, state, and institution levels are essential, as is vendor accountability and in-house oversight.

A central issue for learning technologists and policymakers will be determining which elements of a learner’s educational context should be shared with AI systems, which data must remain private, and how these boundaries can be enforced.

Learn more:

• AI and Education: Protecting the Rights of Learners
• Student Privacy and Ed Tech
• Data Ethics: Privacy, Policy, and Architecture

Learning technologies can help level the learning playing field across demographics, geographies, and job sectors, including through AI-powered tools that offer

• Personalized learning systems that can help all learners advance
• STEM-based education aimed at reducing gender and socioeconomic gaps
• Support for immigrant students in schools, including with language learning

However, AI-based tools can also perpetuate stereotypes and exacerbate existing gaps in digital access by reproducing underlying biases in training data. This reinforces inequalities and disadvantages learners based on race, socioeconomic status, and learning styles.

To address these issues, inclusion, equity, and access must be non-negotiable priorities in learning technology policies and practices.

Learn more:

• AI and Education: Guidance for Policy Makers
• Bridging the Digital Skills Gap for Future Innovators
• AI Integration in Vietnamese Primary Education

Regulation and policies are essential to guiding how and when AI is used in education and training. From data privacy and transparency to equity and access, the issues involved are complex. And a balance is needed between ensuring protections and enabling the innovations that can bridge equity gaps and help all learners thrive.

As a first step, a recent study by the Brooking Institution’s Center for Universal Education notes that educational organizations can help ensure that learning technology serves the needs of students and teachers by pooling their purchasing power.

For example, school districts across the U.S. could agree on criteria for tools and systems that include vetted content and an emphasis on interactive learning and other approaches that ensure students learn in a way that empowers rather than pacifies.

Learn more:

• Who Benefits from AI? The State of AI Governance
• Ethical Considerations of AI Integration in Education: Insights from the Philippines
• Disability Discrimination: Technology Accessibility
• Effective and Ethical AI Implementation: What Educators Need to Know

Stay up-to-date with the latest on learning technologies by accessing our Tech News blog, which is updated daily with the insights, trends, and research related to all things computing. Among recent related articles are the following:

• The Governance Gap: AI, Risk, and Enterprise Innovation
• From Isolation to Innovation: Establishing a Computer Training Center to Empower Communities
• Bridging Digital Infrastructure, AI and Education in Sri Lanka
• Innovate AI Hackathon: Personalized Learning Systems
• Innovations in Education: Gerald Jay Sussman Interview
• Entry-Level Accessibility Barriers: A Challenge For Immersive Technologies