Authoring tools in learning, or eLearning authoring tools, are software applications used to create, structure, manage, and publish digital learning content and interactive educational experiences. They are used in online learning, corporate training, higher education, K–12 education, workforce development, instructional design, and professional certification programs. These tools support the development of online courses, simulations, quizzes, assessments, branching scenarios, multimedia lessons, software demonstrations, and adaptive learning environments.

History

Early computer-based learning The first generation of learning authoring systems emerged during the 1980s and early 1990s alongside the growth of personal computing and enterprise software training. These early systems were commonly associated with computer-based training (CBT), which delivered educational content through separate desktop applications distributed on floppy disks, laserdiscs, and CD-ROMs.Rosenberg, M. J. E-Learning: Strategies for Delivering Knowledge in the Digital Age. McGraw-Hill, 2001.Parsloe, E., & Oates, M. Training and Development. Kogan Page, 2004.Goodman, D. The Complete HyperCard Handbook. Bantam Books, 1988.

Early CBT authoring systems focused primarily on procedural instruction, compliance training, technical skills development, and military education. Content structures were generally linear and text-heavy, with limited interactivity beyond multiple-choice assessments and knowledge checks. Graphics, animations, and audio capabilities were constrained by the hardware limitations of the period.Ally, M. Foundations of Educational Theory for Online Learning. Athabasca University Press, 2008.

Authoring systems during this era often required programming knowledge or scripting skills. Organizations developing CBT content commonly relied on specialized instructional technologists and software developers. CBT systems were widely used in aviation, manufacturing, healthcare, government, and defense sectors. During the late 1980s and early 1990s, multimedia authoring platforms such as Macromedia Authorware, ToolBook, HyperCard, and Director introduced greater support for interactive media, simulations, and graphical interfaces. These tools allowed instructional designers to incorporate audio narration, animations, video clips, and branching navigation structures into digital training materials.Macromedia Authorware and Adobe Director discussed in: Vaughan, T. Multimedia: Making It Work (6th ed.). McGraw-Hill, 2003.Fletcher, J. D. Computer-Based Instruction History, Industry, and Applications. Praeger, 1990.

Web and SCORM-based eLearning The expansion of the internet during the late 1990s and early 2000s shifted eLearning development from locally installed CBT systems toward browser-based online learning environments. Learning management systems (LMSs) became more common in higher education and corporate training, increasing demand for interoperable digital learning content.

During this period, standards organizations developed specifications intended to improve compatibility between authoring tools and LMS platforms. The Sharable Content Object Reference Model (SCORM), introduced by the Advanced Distributed Learning Initiative in 2000, became one of the most widely adopted interoperability standards in eLearning. SCORM-enabled course packages to communicate learner progress, completion data, and assessment results between content and LMS platforms. The adoption of SCORM contributed to the growth of commercial eLearning authoring software, including Adobe Captivate, Lectora, and Articulate products such as Articulate Presenter and later Articulate Storyline. These systems enabled instructional designers to create slide-based online courses containing narration, quizzes, animations, screen recordings, and multimedia interactions. Instructional workflows during this period generally remained content-centered and sequential. Courses were often structured as page-by-page learning modules designed for desktop computers. Visual consistency, assessment tracking, and LMS compatibility became central priorities for eLearning production teams.Dodds, P., & Thropp, S. SCORM 2004 4th Edition Overview. ADL Initiative, 2009.Allen, M. W. Michael Allen's Guide to E-learning (2nd ed.). Wiley, 2016.

The rise of Adobe Flash during the 2000s also influenced eLearning authoring practices. Most of the authoring platforms relied on Flash-based interactive content until the decline of Flash support in the 2010s prompted a migration to HTML5 publishing standards.

Responsive and cloud-based authoring The growth of smartphones, tablets, cloud computing, and software-as-a-service platforms during the 2010s led to major changes in eLearning authoring technologies. Organizations increasingly require learning content that could function across desktop and mobile devices, leading to greater adoption of responsive design approaches. Cloud-based authoring systems introduced collaborative editing, shared asset libraries, template-based workflows, and browser-based publishing environments. These systems reduced reliance on locally installed software and supported distributed instructional design teams. Authoring platforms such as Rise 360, Genially, and H5P emphasized reusable templates, multimedia integration, responsive layouts, and simplified content creation workflows. Interactive content formats expanded to include scenario-based learning, gamification systems, drag-and-drop activities, branching pathways, and microlearning modules. The 2010s also saw increased focus on learner engagement, user experience design, accessibility standards, and data analytics. Learning experience platforms (LXPs), mobile learning systems, and xAPI-based tracking frameworks contributed to the spread of experimentation with personalized and data-driven learning environments.Kukulska-Hulme, A., & Shield, L. "An Overview of Mobile Assisted Language Learning." ReCALL, Cambridge University Press, 2008.Ally, M., & Tsinakos, A. Increasing Access through Mobile Learning. Commonwealth of Learning, 2014.Brown, M., et al. "The Rise of Learning Experience Platforms." EDUCAUSE Review, 2020.

AI-Powered learning authoring systems The expansion of generative artificial intelligence technologies during the 2020s introduced new categories of AI-assisted and AI-driven learning authoring systems. These systems used large language models (LLMs), generative media systems, and automation tools to accelerate content production and modify instructional workflows.

Traditional authoring tools with AI layers Most of the established eLearning authoring platforms integrated artificial intelligence as an additional feature layer within existing workflows. In these systems, AI capabilities typically assisted specific production tasks rather than restructuring the broader instructional architecture. Common AI-assisted functions included automated text generation, quiz creation, translation, summarization, voice synthesis, image generation, and content recommendations. AI functionality was frequently incorporated into traditional slide-based course development workflows. Examples included AI-related features introduced into Articulate products, Adobe Captivate, and learning management systems incorporating generative AI assistants. In these environments, instructional workflows generally remained manually orchestrated and centered on conventional course production structures. Artificial intelligence functioned primarily as a productivity enhancement layer within pre-existing authoring models.Holmes, W., Bialik, M., & Fadel, C. Artificial Intelligence in Education. Center for Curriculum Redesign, 2019.Luckin, R., et al. Intelligence Unleashed: An Argument for AI in Education. Pearson, 2016.

AI course creators Another category of systems focuses primarily on accelerating the generation of educational content through large language models and automated drafting systems. These platforms were commonly referred to as AI course creators. AI course creators typically allow users to upload source documents, generate outlines, create quizzes, summarize materials, produce microlearning content, and automate portions of course writing. Their primary objective was to reduce manual content development time. Examples of platforms associated with this category include Coursebox, Easygenerator AI features, LearnWorlds AI integrations, and AI-assisted LMS content generators. These systems emphasized speed and automation in instructional content production. However, a majority remained dependent on conventional learning structures and required additional manual editing, instructional review, and workflow coordinations.

General-purpose LLM learning generation The development of large-scale generative AI systems during the 2020s also enabled general-purpose AI platforms to function as flexible educational content generation environments. Systems such as OpenAI's ChatGPT, Anthropic's Claude, and Google's Gemini became increasingly capable of generating educational materials directly from user prompts. These systems could generate lessons, quizzes, instructional scripts, summaries, translations, role-play scenarios, and learning activities across different subjects and formats. Their flexibility enabled widespread experimentation among educators, instructional designers, and corporate learning teams. However, general-purpose LLM platforms were not designed specifically for structured learning operations such as learner analytics, SCORM packaging, LMS interoperability, instructional governance, assessment orchestration, or enterprise compliance management. Their educational applications, therefore, often required additional human review and integration with external learning systems.Bommasani, R., et al. On the Opportunities and Risks of Foundation Models. Stanford Center for Research on Foundation Models, 2021.

AI native authoring systems AI-native authoring systems emerged during the mid-2020s as a category in which artificial intelligence was integrated directly into the broader instructional workflow rather than added as an isolated productivity feature. These systems focus not only on accelerating content generation but also on coordinating interactive learning creation, simulations, adaptive pathways, conversational learning systems, collaborative editing environments, learner analytics, deployment workflows, and connected learning ecosystems. Some AI-native systems experimented with conversational interface design, human-in-the-loop workflows, governance controls, source-of-truth architectures, AI tutoring systems, and automated orchestration of instructional design processes. Examples in this category included platforms such as Mindsmith and Mexty, as well as other AI-native interactive learning systems developed during the 2020s. AI-native systems generally focus on workflow simplification, learner engagement, adaptive learning, governance, auditability, and integration across several stages of instructional development. The emergence of these systems reflected a broader transition from AI systems focused primarily on generating learning content toward systems intended to coordinate interactive learning environments and educational workflows.Amershi, S., et al. "Guidelines for Human-AI Interaction." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2019.

Governance and regulations