Until recently, AI authoring mainly meant creating content quickly. Generating text, formulating quiz questions, creating images, or translating courses. That saves time—but it no longer goes far enough. With AI agents and open interfaces like MCP, the next stage of development is beginning: AI is evolving from a standalone feature to an active component of the learning design process. For L&D teams, the question is therefore no longer just which tool produces good content, but how intelligently it can be integrated into existing processes and systems.
Introduction: Why AI-Powered Content Creation Is No Longer Optional
For decades, developing learning content has been one of the biggest challenges in corporate learning—it is time-consuming, costly, and difficult to scale. At the same time, content today must be available more quickly, be multilingual, and be easy to update.
AI-powered authoring tools are fundamentally changing this process: They speed up content production, assist with translations, and enable L&D teams to scale learning offerings much more efficiently.
However, the field has now evolved beyond mere content generation. AI agents are increasingly able to support entire steps in the instructional design process—from analyzing existing sources to defining learning objectives and creating storyboards, all the way to revising existing content. At the same time, open interfaces such as the Model Context Protocol (MCP) make it possible to connect authoring tools directly to AI assistants like ChatGPT or Claude.
This also changes the question of what a modern authoring tool should be capable of.
Tool Comparison: A Market Analysis
When selecting an AI authoring tool, scalability, ease of use, translation features, and integration with existing learning ecosystems remain key factors.
Today, there are additional criteria to consider: How effectively does AI support the entire authoring process? Can the tool integrate with other AI systems? And can content, analytics, and workflows be connected?
- Mindsmith: AI agent, MCP integration, translation, Dynamic SCORM, and analytics—particularly well-suited for scalable, AI-integrated content creation.
- Elucidat: Templates, team permissions, and extensive translation workflows—especially for larger content teams.
- Easygenerator: Quick course creation and easy to use—well-suited for smaller teams and straightforward learning content.
- iSpring Suite: Tight PowerPoint integration and scenario tools—ideal for PowerPoint-based workflows.
- Adobe Captivate: Simulations and high customizability—for more complex e-learning projects.
- Articulate Rise 360: Responsive design and intuitive operation—for quickly creating responsive learning content.
The differences are increasingly no longer limited to the editor itself. Another key factor is how well a tool can be integrated into existing learning, data, and AI workflows.
"The exciting development isn't that AI creates content faster. What matters is that agents are increasingly supporting the entire learning design process—and doing so while seamlessly integrating with existing systems."
Sergio Cerminara, Senior Customer Success Manager at MaxBrain
In the Spotlight: Mindsmith
Mindsmith is now integrating traditional authoring features more closely with AI-powered workflows.
What sets Mindsmith apart:
- Dynamic SCORM: Content can be updated and kept in sync with the LMS.
- Translation and Localization: Multilingual content can be managed centrally.
- Custom Branding: Learning content can be customized to match the corporate design.
- Analytics: Usage, completion, scores, and engagement can be analyzed.
- Agent: The agent does more than just help generate text. It analyzes sources, asks follow-up questions when necessary, develops learning objectives and storyboards, and can edit existing content in a targeted manner.
- MCP Integration: Mindsmith can be connected to compatible AI assistants such as ChatGPT, Claude, or Cursor. This allows you, for example, to search for and create lessons, view analytics, or edit content directly from within the AI assistant.
This last point, in particular, shows the direction in which authoring is evolving: away from a standalone tool and toward an integrated component of the digital workplace.
From AI-Powered Authoring to Agentic Learning Design
In 2025, many AI tools were still focused on the question: How quickly can AI generate content?
Today, another question is becoming more important: To what extent can AI meaningfully support the entire process?
For example, an agent can analyze sources, suggest a didactic structure, ask follow-up questions, and only then create the actual learning content. Instructional designers thus provide fewer individual prompts and focus more on guiding objectives, quality, and framework conditions.
People remain at the center of this: learning objectives, subject-matter accuracy, tone, and final quality must continue to be clearly assigned.
MCP: Authoring Becomes Part of the AI Ecosystem
Another important development is the Model Context Protocol (MCP). Simply put, MCP enables AI assistants to communicate in a controlled manner with other applications and their functions.
For L&D, this opens up new workflows. Instead of switching between multiple applications, a team can, for example, do the following directly from an AI assistant:
- find existing learning content,
- Analyze performance data,
- create new lessons or edit existing ones,
- Publish content or generate tracking links.
This makes interoperability a new selection criterion for learning technologies.
Key Findings
AI-powered content creation boosts efficiency and reduces repetitive work. Translation and localization features remain crucial for global scaling. At the same time, tools continue to differ significantly in terms of user experience, collaboration, and integration capabilities.
In addition, there are three new factors:
- Agent Capability: Does the AI support individual tasks or the entire authoring process?
- Interoperability: Can the tool work with other AI systems and enterprise applications?
- Data Integration: Can analytics be used directly to further develop learning content?
There is still no one-size-fits-all solution. The key factor is which tool is the best fit for your team, learning ecosystem, and use case.
How to Choose the Right AI Writing Tool
When making your selection, ask yourself the following questions:
- How large is your team, and how much technical experience do the authors have?
- Is there a need for multilingual content?
- How well can the tool be integrated into your existing learning platform?
- What are the requirements for branding and collaboration?
- Can AI also assist with more complex tasks such as research, storyboarding, and content review?
- Can the tool be integrated into existing AI workflows via MCP, APIs, or other interfaces?
- Can usage data be used directly to optimize content?
As a result, the focus is increasingly shifting from individual features to the question of how well a tool fits into the overall learning technology landscape.
A Look into the Future: From Authoring Tool to Learning Platform
Some developments that were still a distant dream just a few years ago have already become a reality. Today, AI can not only generate content, but also analyze sources, create storyboards, review content, and interpret performance data.
The next stage of development is therefore unlikely to focus on generating text even faster. What will be crucial is how well agents, corporate knowledge, authoring, analytics, and existing systems work together.
For L&D teams, this means that the biggest leap in productivity doesn't come from yet another AI button, but from intelligently connected workflows.
Frequently Asked Questions
What is an AI agent in learning design?
An AI agent does more than just assist with individual tasks, such as writing texts. It can break down a work assignment into several steps, analyze sources, ask follow-up questions, develop learning objectives and storyboards, and assist with the creation or revision of learning content.
What is MCP?
The Model Context Protocol (MCP) is an open standard that allows AI applications to connect to external tools and data sources. This enables AI assistants such as ChatGPT or Claude to access features of a connected authoring tool, for example.
What does Mindsmith's MCP integration offer?
Mindsmith can be connected to compatible AI assistants via MCP. This allows users, for example, to find projects and lessons, analyze analytics, and—depending on their permissions—create, edit, or publish content.
What is the difference between AI authoring and agentic authoring?
In traditional AI authoring, AI primarily assists with individual tasks, such as generating text, images, or quiz questions. Agentic authoring takes it a step further: The AI guides users through a multi-step process and assists with tasks such as research, planning, creation, and revision.
Will AI Agents Replace Instructional Designers?
No. AI agents can take over repetitive and time-consuming tasks and assist in structuring learning content. However, learning objectives, subject-matter quality, instructional decisions, and final quality assurance remain the responsibility of humans.
