Keeping Humanity In AI-Enhanced Learning Design

Balancing AI Efficiency With Human-Centered Design

In eLearning’s embrace of AI, like with our very own learners, there is the understanding that the human comes first. In Machine Learning, this is the human-in-the-loop (HITL), where humans help the machine make the correct decisions. In Instructional Design, this is the understanding that the designer imbues their humanity into their coursework to ensure a relatable, accurate, engaging learning experience, and not merely an efficient production.

The relationship between AI efficiency and human creativity does not need to be a struggle, but it can be a balance. It need not be adversarial; it can be complementary. AI can accelerate workflows and surface insights, and humans can ensure learning remains meaningful, ethical, and emotionally resonant. Here are a few common concerns a designer faces when working with AI and ways that human-in-the-loop mentality can ensure an immersive and authentic experience for the human learner.

Concerns Mitigated By The Human Factor In AI-Enhanced Learning Design

1. Creativity

While AI is faster than the humans who created it, it may not be more creative than the humans who created it. It combines existing patterns, but there is no creative synthesis. It can generate variations, but it cannot originate meaning, emotion, or intent. It processes; it doesn’t imagine.

AI can accelerate production, uncover patterns, and even spark ideas humans may not have seen, but it cannot discern why something matters or for whom it should exist. That is an understanding of the learner and the learner’s needs. That interpretive layer (context, empathy, and storytelling) is purely human. The most effective designs use AI as a co-creator, not a replacement, letting the machine generate possibilities while the human shapes purpose and story. This creativity is what keeps the learning engaged and motivated; it gives learning authenticity, emotional resonance, and motivational spark. Keeping the human in “human-centered design” includes the designer.

2. Personalization

AI systems often promise “personalized learning,” but, in practice, this personalization frequently relies on surface-level engagement metrics, such as click rates or completion times, rather than deeper evidence of cognitive understanding. The result is learners may know what to do, but not how to apply it. [1] Due to the influence of algorithmic “glazing,” [2] learners can receive recommendations that reinforce existing strengths, rather than addressing genuine skill gaps.

Without expert oversight, AI can misdiagnose learner needs and preferences, resulting in pseudo-personalization rather than genuine adaptation. This is not personalized learning in the Instructional Design sense; rather, it’s a one-size-fits-all model masquerading as customization. Skilled Instructional Designers counter this by using adaptive frameworks, branching scenarios, and flexible RTI (Response to Intervention) design that change with the learner, not around them.

3. Voice

An AI voice in writing has similarly identifiable clues as visual AI, and once you begin to spot them, they become glaringly, suspiciously, disengagingly evident. There’s the sycophancy, passive voice, and the abundance of em dashes. Just as bad editing in film takes the viewer out of the experience, an awareness you’re reading AI content takes the learner out of the learning experience. This is why the ever-present reminders that AI is just a tool in the hands of experts are necessary: it’s up to the human to ensure their voice, and not the machine’s, is evident in the learning experience they’re designing.

Be aware of the common pitfalls of AI voice and edit accordingly. Read it aloud. Have a peer review it. Add personality: stories, anecdotes, actual office pictures, etc. This involves a dependence upon the organization’s style guide as a source of truth, reducing business jargon, and reading the output as if you were accountable for it (because you are accountable for it). AI can accelerate production, but it cannot replicate human warmth or intent. Maintaining that distinction preserves trust and keeps learners immersed in the experience you designed.

4. Accountability

When the AI is mistaken, as it is statistically wont to do, [3] who catches the error and who owns the responsibility? Generative AI tools can produce plausible but incorrect or obsolete information. If the AI models train on outdated or biased data sources, then those beliefs can be smoothed into a new context to be perpetuated to a waiting audience, possibly impacting assessments, recommendation systems, and hiring-related training outcomes. For global or DEI-focused programs, this can lead to unfair learning pathways or content visibility that disadvantages certain learner groups. AI-enhanced platforms can unintentionally widen accessibility gaps if training data or design choices don’t represent diverse learners.

Human designers must audit for equity and ensure learning technologies are inclusive, truthful, and welcoming by design. Without rigorous Instructional Design oversight, training materials can contain subtle errors, copyright issues, or pedagogical flaws. Whether hallucinations, inaccuracy, or misinformation, errors can compile to become an enormous liability and reputational risk, one which human Instructional Designers, learning developers, Subject Matter Experts, quality assurance analysts, and fact checkers mitigate. Ultimately, accountability cannot be outsourced; responsibility for accuracy and integrity always rests with the human team.

5. Transparency

While AI-generated content may introduce errors into learning systems, it may also introduce proprietary information or copyright violations, [4] again exposing the organization to serious risk. As AI systems are trained to create new content, they may draw too closely from proprietary sources, leading to issues with plagiarism or intellectual property rights.

Learners should be informed when the content they are engaging with is AI-generated. Ethical concerns arise when AI is used without transparency, as learners may feel misled if they believe materials were crafted exclusively by industry experts, only to discover they were produced by AI. Ethical use of AI in content creation requires clear transparency, rigorous human review, and institutional accountability.

AI’s role in learning should mature through continuous human feedback. Iteration, not automation, sustains quality and relevance. AI may scale what we create, but it is human intention that gives learning its meaning. The goal is not to remove the human from the process, but to magnify the human contribution through intelligent partnership. The future of learning will belong not to the fastest systems, but to the most thoughtful collaborations.

References:

[1] The Age of De-Skilling

[2] The Glazing Effect: How AI Interactions Quietly Undermine Critical Thinking

[3] Largest study of its kind shows AI assistants misrepresent news content 45% of the time

[4] The Dangers of Using AI to Write Training Course Materials