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Managing Expectations: A Leadership Skill Every Freelancer Needs

A successful project does not begin with the first course page, video, or learning activity. It often begins with a conversation about expectations.

Clients may come to an instructional designer with a deadline, a collection of content, and a general idea of what they want. Administrators may have one set of priorities while faculty or subject matter experts have another. Part of our job is helping everyone develop a shared understanding of what can realistically be accomplished.

That requires leadership.

Setting expectations early can prevent problems later. What will be delivered? Who is responsible for reviewing the work? How many rounds of revisions are expected? When does feedback need to be provided? What happens if content arrives late or the project changes direction?

These questions may not be exciting, but they can make the difference between a smooth project and a frustrating one.

AI can help make this process easier. For example, AI can turn meeting notes into a draft project summary, organize responsibilities, create an initial timeline, or help identify questions that still need answers. It can also help compare feedback from several stakeholders and organize requested changes before the next project meeting.

But AI should support communication rather than replace it. If a deadline needs to change or two stakeholders want different things, sending an automated message may not be enough. Sometimes the best leadership tool is still a direct conversation.

Freelancers also need to be comfortable saying when something is unrealistic. Agreeing to every request may feel like good customer service, but it can lead to rushed work, missed deadlines, and disappointment. A professional response explains the concern and helps identify another path forward.

Good leadership does not mean always saying yes. It means helping people understand the choices in front of them and keeping the project focused on its goals.

Clear expectations at the beginning can prevent difficult conversations at the end.

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From Learning Objectives to Assessments: Making Sure Everything Connects

A well-designed course should tell a clear story. Learners should understand what they are expected to learn, have opportunities to practice it, and then be asked to show what they know.

That sounds simple, but it is easy for those pieces to become disconnected.

A course might have a learning objective that asks students to analyze information, while the final quiz only asks them to remember definitions. Another course might ask learners to create something but never give them a chance to practice that skill before the final assignment.

This is where instructional design becomes especially valuable.

Learning objectives provide a starting point. From there, designers can ask what learners would need to do to demonstrate that they have reached each objective. If the objective asks learners to compare two ideas, the assessment should give them an opportunity to compare. If learners are expected to solve a problem, they should eventually be asked to solve one.

Activities, readings, videos, discussions, practice exercises, and assessments can then be built around those goals. When these pieces support one another, learners have a much clearer path through the course.

AI can make this process more efficient. An instructional designer might use AI to review a set of objectives and suggest possible assessments, practice activities, or discussion questions. AI can also help identify possible gaps between what an objective says and what an assessment actually measures.

That does not mean we should allow AI to make the final decisions. Context matters. The designer and subject matter expert still need to decide whether an assessment is appropriate for the learners, course level, subject, and learning environment.

For freelancers working with subject matter experts, this can be one of the most useful conversations we bring to a project. Instead of asking only, “What content should we include?” we can also ask, “What should learners be able to do when this is over?”

Once that question has a clear answer, designing the rest of the learning experience becomes much easier.

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Before You Add Another Tool: Evaluating Educational Technology That Actually Helps

There is always another educational technology tool promising to make teaching easier, learning more engaging, or course development faster. For an instructional designer, especially one working with several clients, it can be tempting to jump in and try the newest thing.

A better approach is to slow down and ask a few practical questions.

What problem does the technology solve? Is it easy for faculty and students to use? Is it accessible to people with disabilities? Does it work on different devices? What happens to the information users enter into it? And perhaps most importantly, does the tool improve the learning experience enough to justify adding another piece of technology?

Cost matters, too. A free tool may seem like an easy choice until important features move behind a paid plan. A more expensive product may actually be the better value if it replaces several other tools or saves faculty and staff many hours of work.

Artificial intelligence adds another layer to this evaluation. Many educational products now advertise AI features, but the words “powered by AI” do not automatically mean a product is better. Ask what the AI actually does. Does it solve a real problem? Does it save meaningful time? Can users review and correct what it produces? How does the company handle the information entered into the system?

Freelancers also need to remember that we are often guests in someone else’s technology environment. A university, nonprofit organization, or business may already have approved systems, security requirements, accessibility standards, and support processes. Introducing another tool may create more work rather than less.

Sometimes the best educational technology decision is adopting something new. Other times, it is discovering a better way to use what the organization already has.

Good technology should remove barriers, not create new ones.

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Keep It Simple: Using Technology Without Overwhelming Learners

There is no shortage of technology available for teaching and learning. Discussion tools, polling systems, interactive activities, video platforms, collaboration spaces, and AI tools can all add something useful to a course.

But more technology does not always mean better learning.

Every new tool asks learners to do something. They may need to create an account, learn a new interface, remember another password, or figure out how to submit their work. When too many tools are introduced, the technology itself can become a barrier.

A better approach is to start with the learning goal. If you want learners to share ideas, perhaps a simple discussion or polling tool is enough. If you want them to work together, a shared document may accomplish the goal without introducing an entirely new platform.

Low-stakes activities are also a great place to introduce technology. A quick poll, short practice quiz, or simple reflection gives learners an opportunity to become comfortable with a tool before they are asked to use it for an important assignment.

Artificial intelligence adds even more possibilities. AI can help instructors create practice questions, develop short scenarios, provide alternative explanations, or generate ideas for interactive activities. Learners may also be able to use approved AI tools to practice skills or explore a topic in greater depth.

The key word is approved. Before introducing any outside technology, instructional designers and faculty should consider accessibility, privacy, security, cost, and the institution’s technology policies. A free tool is not necessarily free if learners must provide personal information to use it.

Technology should support the learning experience without becoming the learning experience.

Sometimes the smartest use of technology is choosing the simplest tool that gets the job done.

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More Than Logging In: Creating Meaningful Interaction in Online Courses

An online course can have great videos, clear instructions, and well-designed assignments and still feel disconnected. Learners need opportunities to interact with their instructor, the course content, and, when appropriate, one another.

This is where Regular and Substantive Interaction, often called RSI, becomes important. But meaningful interaction should be more than something added to a course to meet a requirement. Done well, it can help learners feel supported and connected throughout the learning experience.

Interaction does not always need to be complicated. An instructor might provide feedback on an assignment, post a weekly announcement about common questions, participate in a discussion, offer virtual office hours, or send a message to learners who appear to be struggling. Small and consistent interactions can have a big impact.

Instructional designers can help faculty build these opportunities into a course from the beginning. Instead of waiting until the course is finished and asking, “Where can we add interaction?” consider interaction while designing each module. Where might learners need guidance? When would instructor feedback be most useful? Where could a discussion help learners think more deeply about the topic?

AI can support this work without replacing the instructor. It can help draft discussion prompts, summarize common questions, organize student feedback, or suggest different ways to explain a difficult concept. An instructor can then review and personalize that information before sharing it with learners.

The goal should not be to automate the relationship between instructors and students. In fact, AI may be most useful when it reduces routine work and gives instructors more time for meaningful interaction.

Online learning becomes much more personal when learners know there is a real person on the other side of the screen.

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Your Portfolio Should Tell a Story, Not Just Show Your Work

For an instructional designer or learning experience designer, a portfolio can be one of the most important parts of a professional website. It gives potential clients and employers a chance to see what you can do. But a strong portfolio should do more than display attractive course pages, videos, or interactive activities.

It should help tell the story behind the work.

For each project, consider providing a little context. What problem were you trying to solve? Who were the learners? What was your role? Did you work with subject matter experts, faculty, administrators, or other designers? What decisions did you make, and why?

This information can be especially important for freelancers. Much of our work happens as part of a larger team, so the finished product may not tell someone exactly what we contributed. A short explanation can show the thinking, collaboration, and problem-solving that happened behind the scenes.

Artificial intelligence can help when building or updating a portfolio. AI can help turn project notes into short descriptions, suggest ways to explain technical work in plain language, or help organize projects into useful categories. It can also help identify repeated words or descriptions that may make several portfolio items sound too similar.

There is also a need for caution. Client work may include private information, student information, copyrighted materials, or content covered by a contract. Before sharing anything publicly, make sure you have permission to do so. When necessary, create a sample or remove identifying information rather than posting the original work.

A portfolio does not need to include everything you have ever created. A smaller collection of well-explained projects can often say much more about your abilities than dozens of examples without context.

Show the work, but also show the thinking that made the work possible.

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Leading Without the Title: Building Influence on a Project Team

Freelance instructional designers often find themselves in an interesting position. We may be responsible for moving a course or training project forward, but the people we work with do not report to us. The team might include subject matter experts, faculty, technology staff, other designers, and administrators.

In these situations, leadership is less about authority and more about influence.

One of the best ways to build that influence is to make the project easier for everyone else. Set clear expectations, communicate regularly, keep meetings focused, and follow through on commitments. When people know they can depend on you, trust begins to grow.

Listening is equally important. Subject matter experts know their content. Faculty understand their students. Administrators may understand needs and limits that are not obvious to the rest of the team. A good instructional designer brings these different viewpoints together rather than trying to control every decision.

Artificial intelligence can help with some of the behind-the-scenes work. AI can summarize meeting notes, organize feedback, identify action items, draft project updates, and help prepare questions for upcoming meetings. This can give the designer more time to focus on relationships, decisions, and the quality of the learning experience.

AI can also help when several stakeholders provide different feedback. A designer might use AI to organize comments by topic or identify areas where reviewers appear to agree or disagree. The designer can then review the results and decide what needs further discussion.

Technology, however, cannot build trust for us. People still want to know that they have been heard and that their expertise is respected.

You do not always need a management title to lead a project. Sometimes leadership simply means helping a group of people move in the same direction.

When you bring clarity, consistency, and respect to the work, people are more likely to follow your lead.

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Designing for Everyone: Making UDL Part of the Process

Good instructional design should make learning easier to access, understand, and use. That does not mean making a course easier. It means removing unnecessary barriers that can get in the way of learning.

Universal Design for Learning, often called UDL, provides a useful way to think about this. Rather than creating one learning experience and trying to fix accessibility problems later, UDL encourages us to think about different learners from the beginning.

For example, important information might be available as text, video, and audio. A video might include captions and a transcript. Learners might have more than one way to practice a skill or show what they know. Instructions should be clear, navigation should be predictable, and course materials should work with assistive technology whenever possible.

Artificial intelligence can help with some of this work. AI tools can provide a starting point for transcripts, descriptions, summaries, alternative explanations, and simplified versions of complicated content. AI can also help instructional designers identify areas that may need additional review. However, AI-generated materials still need human attention. A transcript can contain errors, an image description can miss an important detail, and simplified language can sometimes change the original meaning.

For freelancers and contractors, UDL can also help guide conversations with subject matter experts. An expert may know the content extremely well but may not have considered how a learner with a disability, limited technology, or less background knowledge will experience it. Asking these questions early can prevent major revisions later.

Accessibility should not be something we check off at the end of a project. When accessibility and UDL are built into the design process, they often make the learning experience better for everyone.

Designing for more learners from the beginning usually means fixing fewer problems at the end.

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Personalized Learning with AI: Start with the Learner, Not the Technology

Artificial intelligence is opening new possibilities for personalized learning. Instead of giving every learner the exact same experience, AI tools can help provide different types of support based on individual needs. But as with most educational technology, the tool should never become more important than the learner.

Personalized learning can be surprisingly simple. A learner who understands a topic quickly might be directed to a more challenging activity. Someone who is struggling might receive another explanation, an example, or additional practice. AI can help make these options easier to create and manage.

Instructional designers can also use AI during the development process. For example, an AI tool might help create several versions of a practice activity at different levels of difficulty. It could suggest examples for different audiences or help turn a complicated explanation into simpler language. The instructional designer can then review and improve those materials before they reach learners.

There are also important questions to consider. What learner information is being collected? Where is that information stored? Is the AI tool accessible? Are its recommendations accurate? Faculty, administrators, technology teams, and instructional designers may all need to be part of these conversations.

For freelancers and contractors, this is especially important. We may enter a project excited about a new technology, but our job is not simply to introduce the newest tool. Our job is to understand the organization, the learners, and the goals of the project.

AI can help us create more flexible and personalized learning experiences. But good instructional design still begins with a simple question:
What does this learner need in order to succeed?

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How AI Can Make Technology Work Smarter for You

Technology should make your life easier, not more complicated. But with so many tools out there, it’s easy to end up juggling multiple platforms, passwords, and processes. That’s where AI can help tie everything together.

AI can connect the tools you already use so they “talk” to each other. For example, if your learning management system (LMS) and your video conferencing tool don’t share data, an AI integration can transfer attendance, participation scores, or assignments automatically. This saves you from doing double work.

AI can also handle repetitive tasks, like sending reminders to students, creating summaries of recorded lessons, or organizing resources into categories. These little things may not seem like much on their own, but over time they save hours — hours you can spend on the creative, high-impact parts of your job.

Even better, AI can help you spot which technologies aren’t pulling their weight. By tracking how often learners use a tool and what results it produces, AI can give you the hard data you need to decide whether to keep it or replace it.

When you let AI handle the “busy work” of technology, you can focus on what matters most — creating great learning experiences.