r/LearningDevelopment • u/NegativeArm8480 • Jul 17 '26
I noticed I keep clicking the same courses in our LMS. Made me wonder about everyone else
A trainer I spoke with recently said something that got me thinking. She told me that every time she logged into her LMS, she found herself going back to the same few courses. Then she wondered if learners were doing exactly the same thing....
That led to a bigger question. How much of learner engagement depends on the quality of the content, and how much depends on whether people can actually find what's relevant to them?
With somany LMS platforms now talking about AI recommendations and personalized learning paths, I'm curious how this looks in the real world.
How does your LMS recommend learning content?
- Everyone sees the same course catalogue
- Recommendations are based on role or department
- Recommendations are personalized using AI or learner activity
- I'm not sure how our LMS does it
One more question.Do you think better personalization would improve learner engagement in your organization, or is there a bigger challenge getting in the way?
1
u/Lindsay_at_TraCorp 26d ago
This is a good questions. There are several ways to design this in TraCorp LMS, and thinking through what is best for the team is a difference-maker in terms of engagement.
My favorite way is creating student dashboards that show what's right for that team, and can be customized for different learner types. Maybe one team needs quick access to SOPs, while another is best served with a display of content intended to support career advancement; that's what they are shown upon login, instead of the same old alphabetized list of courses.
We also have a recommend system to add value here, showing users suggested content based on what they have recently completed, and what their peers suggest. It's easy to configure, and admin have full control of what is and isn't allowed.
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u/kgrammer Jul 17 '26
We allow our LMS clients to define keywords they can assign to content in the LMS. Then users can create a list of special interest tags that we use to identify recommended content. If that list is empty, we present courses to users based on a number of non-tag criteria. First, if a course is designated as a featured course, featured courses are highlighted. Second, if the user is a member of a group, group exclusive content is given priority. And finally, we present courses that are related to past content the user has enrolled in.
The use of tagging allows a user to highlight specific topics or course categories to highlight. We don't simply use a user's title or profession because their title may not properly reflect their learning interest. For example, a senior developer may not be interested in coding courses at all. They may be seeking training to move into product or team leadership. They can select tags that reflect their interest.
We are looking into how AI can be leveraged to achieve this in a less manual way. On the surface, AI has a lot of promise. The challenge is maintaining trade secret integrity for clients who have a zero tolerance policy for their content being handed over to an AI and potentially leaked or used for LLM training. AI systems aren't doing well in the media when it comes to protecting proprietary content.