Ethical and Legal Implications of AI in Learning Design

Think of a tutor who works tirelessly and knows precisely what you need to learn and at what pace you learn best. AI-powered learning systems are making this a reality, helping students and employees acquire knowledge more efficiently. From real-time personalized lessons to automated content creation that enables instructors to scale their efforts, AI is reshaping education and corporate training.

However, here is the big question, what happens when AI makes a blunder? What if it somehow tries to help but ends up hindering learning opportunities, reinforcing biases, or leading learners the wrong career way? Can we trust AI to make unbiased decisions concerning assessments and recommendations? And who is to blame if an AI-driven system wilfully misuses personal data and breaches privacy laws?

No, these are not just “what if” questions. They are real challenges for educators and organizations to solve today. We have masses of unproven approaches, policies, and rules surrounding AI. In this article, we will examine the ethical and legal aspects of AI in learning including, lack of freedom, privacy, and accountability – to understand the new technology’s risks and promises.

AI in Learning Design: A Double-edged Sword

AI is changing how learning concepts are created and taught. Imagine, instead of a generic training course, an AI system collects data from every user, evaluates their performance, finds missing parts, and suggests content for them instantaneously.

Sounds amazing, right? And in some ways, it is. AI has the capability to:

• Customize the learning journeys based on a learner’s achievements and weaknesses.
• Outsource repetitive tasks such as marking assignments and providing feedback.
• Automatically create materials, providing relief for instructional designers who manually crafted everything.
• Offer real-time data to organizations data, which can be instrumental in evaluating training impact.

Although promising, AI is not perfect.  AI-oriented learning systems can be biased, make unexplainable choices, and pose privacy risks. Such tools give a new meaning to the phrase “Big Brother is watching you”. To use these tools without bias, we will need to examine the ethical problems and AI implications very closely.

The Ethical Implications of AI in Learning Design

AI technology itself is neutral: whether it is beneficial or harmful depends on how we choose to utilize it. Implementing AI technology with ethical considerations involves doing so in a way that guarantees each decision renders fairness and transparency, and puts people at the center.

Bias in AI: When Algorithms Reflect Our Prejudices

Out of all the possible problems that AI can cause in education and training, bias is probably the most serious. Since AI learning relies on historical data, the outputs will always depend on the previous inputs. More often than not, the old data tends to be biased. Therefore, an AI system will not only replicate those biases but also enhance them.

Consider a corporate AI-powered system that tries to identify employees who would benefit from a course in leadership training. If men are more commonly portrayed as leaders, then AI algorithms will recommend them more often than women, which in turn widens the gap.

This isn’t just a hypothesis. Some research conducted by the Stanford Institute for Human-Centered AI found that AI-driven recruiting tools learned from historical hiring data were biased in favor of men. This type of bias within AI systems creates a risk to learning development.

How Can We Reduce AI Bias?

• Diverse and Inclusive Data: Make sure to incorporate people of different genders, races, and learning preferences while creating the AI models.
• Regular Audits: AI systems should be regularly checked to ensure there is no discrimination.
• Human Oversight: AI systems should not make decisions on behalf of people. Rather, these systems should assist human leaders in decision-making. While reviewing AI-powered insights, fairness must be guaranteed by instructional designers as well as educators.

Bias does not always stem from bad intentions. If we do not try to solve this problem, AI will perpetuate past inequities instead of helping to build a more equitable future.

Transparency: The Need for Explainable AI

Have you ever completed an AI-powered test or quiz and asked yourself, Why am I getting this score? ” That’s called the “black box” precision. A lot of decision-making is done by AI systems using irrational means, some of which even their creators do not know about.

This absence of transparency can create difficulties in learning design. If an AI system decides a student is not prepared to proceed in a course, both the student and teacher must understand why that decision was made.

How Do We Make AI More Transparent?

• Explainable AI (XAI): AI algorithms must be configured in a way that they can provide comprehensive reasons for arriving at a certain conclusion or decision.
• User Control: The user of the system should know how the artificial intelligence is modifying their learning experience.
• Feedback Mechanisms: Learners and educators should be able to question or refute AI recommendations. AI systems should be built in a way that makes this possible.

Data Privacy: Who Controls Learner Data

AI-enhanced learning platforms gather enormous amounts of information, such as engagement patterns, quiz scores, time spent on each lesson, and even biometric information in some situations. But, who is the owner? How will it be utilized? And is it in a safe location?

GDPR (General Data Protection Regulation) in Europe and CCPA (California Consumer Privacy Act) in the U.S. has drafted stringent policies about the collection and storage of data. If these laws are not adhered to, the punishment is severe. GDPR fines may soar to €20 million or 4 percent of yearly income.

For an organization to maintain their identity, these steps must be taken:

• Obtain Clear Consent:Notifying learners in detail what data will be gathered and why.
• Minimize Data Collection: Only collect data that is necessary and nothing more.
• Secured Data Storage: User information must be protected with encryption and anonymization.

Protecting data isn’t a legal matter alone, but an ethical one as well. For learners, it is necessary to be assured that the information they share will be kept safe.

Human Oversight: AI Should Assist, Not Replace Educators

While AI can recommend content based on a learner’s progress, it unquestionably lacks the element of human understanding. AI fails to comprehend whether a learner has difficulties due to stress, personal issues, or other influences beyond their control.

This is where human engagement becomes extremely important. AI should assist and make recommendations, but teachers, instructional designers, and trainers should continue to make the most important learning decisions.

Legal Considerations in AI-Driven Learning

At the organizational level, there are other legal ramifications of AI in instructional design that go beyond ethical considerations.

Intellectual Property: Who Owns AI-Generated Content?

From quizzes to complete course modules, AI tools can generate content effortlessly. But as with everything else, content comes with its challenges. Who does this AI content belong to? The AI, the developer, or the organization?

As of now, the most generalized understanding is that AI cannot hold any form of copyright. Mostly, ownership is given to the person or organization utilizing the AI, but this is still a gray area. To resolve issues like these:

• Assign ownership in contracts when using AI-generated content.
• Ensure AI materials do not have copyright material.

Accountability: Who is Responsible When AI Makes a Mistake?

Who is at fault when it comes to an unfairly judged learner or misleading recommendations through an AI-powered learning system? Assuming AI cannot be accountable, the entity utilizing the AI is responsible.

This means businesses and educational institutions:

• Ensure that there is a human guarantee for an AI-powered decision.
• Obtain evidence confirming the existence of documents where the AI made a decision.
• Allow individuals who have been wronged by AI systems a right to appeal to explain their situation.

Best Practices for Ethical and Legal AI in Learning Design

How do businesses like yours ensure that AI improves learning without creating any ethical or legal challenges? The answer isn’t refraining from using AI. It’s about harnessing the technology properly. This means having defined boundaries, incorporating a human decision-maker, and performing regular audits to ensure there are no malfunctions.

Here is how you can make AI-powered learning effortless and ethical at the same time:

Create AI Ethics Guidelines That Mean Something

AI is incapable of morally distinguishing between right and wrong. For most companies that employ the use of AI in learning, the first step is to outline reasonable ethics policies that are simple to follow. Instead of making unnecessarily long and complex documents nobody wants to read, focus on answering these essential questions:

• How do we guarantee that AI-powered learning aids give equal opportunities to all learners regardless of their backgrounds?
• How do we ensure that recommendations made by the AI systems are understandable to both the learners as well as the instructors?
• If a machine makes a mistake, who is responsible?

Most multinationals have policies regarding AI ethics (for example, Google and Microsoft), but such policies need to be customized to suit the specific environment of each institution. A strong ethics policy should be straightforward to implement, rather than just a policy lying around in a manual.

Keep AI and Humans Working Together

Moderation is key to the advancement of AI. It is powerful but imagining a world where it replaces humans as educators for instructors is unrealistic. The ideal experience comes when both man and machine collaborate.

For instance:

• While instructors need to review the recommendations so that they make sense, AI can analyze data to personalize the learning experience.
• AI can automate grading for quizzes, but a human has to tackle all essay and critical thinking assessments.
• AI is capable of creating materials needed for a course; however, an instructional designer must edit and verify the information before it is published.

We don’t want AI to take center stage. The idea is to let AI work in the background like an assistant who helps educators and learners without replacing anyone’s role.

Check AI for Bias and Mistakes (Because It’s Not Perfect)

AI is not some magical flawless being. Always remember that AI learns from data and if not given instructions and structures, it can create and cause harm to society.

Take the example of devising courses that an AI program would create if it were given a database without a proper deep neural network structure. It is likely to create a career development course for men and give women the opposite of that instead of treating both the same.

To mitigate this, companies should:

• Look into decisions made by the AI to see if there are discrimination aspects within the decisions.
• Ensure that the data used when training the model for AI is not biased at all.
• Assign a supervisor to oversee any AI-driven suggestions that might affect the learner’s progress.

Bias often occur involuntarily; nevertheless, without active monitoring, AI will continue to uphold the same outdated biases.

Teach People About AI—Not Just the Tech Team

One of the more glaring blunders that organizations commit is the presumption that the knowledge of AI is the sole jurisdiction of the Information Technology or the Data Science departments. When AI is at work in learning, every single person who plays a role, like the teaching staff, instructional designers, and even the students, need to understand how it works.

This does not mean everyone should become an AI expert, but at the very least:

• Instructors should understand how AI-driven learning recommendations are generated.
• Leaders need to know the challenges linked to data privacy and compliance issues.
• Students need to know what impact AI has on their learning and what they can do to challenge an AI decision they do not agree with.

AI is changing education as we know it, and those who are using it should have a say as to how it operates.

Conclusion

AI in learning design is here to stay, and its potential is enormous. However, the legal and ethical implications of its deployment must not be left unattended while reaping the advantages.

With AI language models fundamentally changing how we learn, it becomes even more important that we focus on fairness, transparency, privacy, and accountability to ensure education is improved, not worsened.

At Auzmor, we advocate for people-first AI. AI’s role in education should not be to replace educators but to assist them. If we adhere to ethical and legal compliance, we can create AI that transforms learning in ways that are just and effective for every single learner.

AI will inevitably transform learning in the years to come, but we should pay careful attention to how this will be done.