Why Most Online Courses Fail—and How AI Can Redesign Completion

Every year, millions of people are spending thousands of dollars on online courses, with the hope to gain new skills, change their career trajectory, or simply improve their day to day lives. However, only 12.6% of those people actually complete the course and get 100% of the value they bought.

I strongly believe that people are not the problem: completion is always a design outcome. If people can’t finish an online course, then there’s an issue with the course, not with the students. In this article, we will dive into the most common flaws in the online courses design, and how AI can fix it.

Un formato non si adatta Tutto

People learn differently. Some need a lot of independence and resources to study alone, others tend to communicate with the professor as much as possible. In order to make the production of an online course as cheap as possible, the content is unified and not adapted for different audiences.

But learners arrive with different backgrounds, personal preferences, and goals.

Beginners may feel overwhelmed by terminology and advanced knowledge, while more senior students will feel slowed down. Without adaptation, many will decide that the course is just not for them, and quietly abandon all efforts to finish it.

Motivation Is Always Temporary

While a desire to learn is crucial in the modern, fast-paced world, most people are not ready for intensive courses that require tons of focus and  independence. There are always motivation spikes and productivity peaks, but it’s very hard to sustain them for the whole duration of the course, which leads to students losing the ability and focus to complete the course. The initial push is very fragile, and needs to be sustained for a prolonged period of time.

Life always gets in the way – KPIs at work, family duties, or simple fatigue – many online course platforms fail to take into consideration that their students are adults with many responsibilities. This leads to many online schools expecting learners to push through long video/text sequences with little feedback or reinforcement.

Psychologists have long argued that willpower is not a reliable long-term strategy. Systems that depend on sustained intrinsic motivation will always fail eventually.

Isolazione sociale

Remember how great college was, and how productive you were? That’s not because college professors are magicians, or because your neural capabilities have decreased. School, college, even corporate webinars – they all give students a sense of community, which is irreplaceable in education. Students need to interact with each other, help each other with the gaps in knowledge, and motivate each other to study harder and longer. Online courses usually fail to deliver the same level of social involvement, which leads to students feeling isolated and alone. Why would you study harder to get an A+ if there will be no friend congratulating you, and relating to your efforts?

In contrast, programs that introduce even minimal social elements, like cohorts, discussion prompts, shared milestones – consistently report higher completion rates. Bootcamps and cohort-based courses often see completion rates several times higher than open-access MOOCs, despite being more demanding. Humans are social learners. When no one notices whether you show up or not, it becomes easier to stop showing up altogether.

Neurodivergenza

An often overlooked part of the completion problem is that many learners are not starting from the same neurological baseline. Neurodivergent traits such as ADHD or anxiety don’t just influence attention spans or stress levels—they directly affect motivation, memory, and the ability to sustain effort over time, especially in self-paced online environments. For these learners, dropping out is rarely a sudden decision; it’s a gradual accumulation of friction, overwhelm, or avoidance.

This is where AI can play a meaningful role, by combining behavioral signals with academic data to identify early patterns that suggest a learner is at risk of disengaging. Crucially, the most effective models don’t leave intervention to algorithms alone. Keeping humans in the loop, qualified coaches who understand both learning psychology and individual blind spots, allows support to be personal rather than generic. When AI surfaces the risk and humans shape the response, support becomes adaptive, empathetic, and far more likely to help learners stay the course.

Così che cosa?

The online education boom has quietly normalized failure. Learners sign up with good intentions, fall behind, and then blame themselves when they drift away, often without realizing that millions of others are doing the same thing. Platforms point to enrollment numbers, universities tout reach, and the gap between promise and reality widens.

The cost isn’t just unfinished videos or unused certificates; it’s the slow erosion of trust in online learning as a serious path to growth. Until course designers start treating drop-off as a design problem rather than a personal one, online education will keep looking successful from the outside while falling short where it matters most.