Rethinking Communication in the Age of Generative AI

Dr. Ceni Babaoglu, Assistant Program Director and Professor, Data Science, Toronto Metropolitan University

Communication is evolving in a world where we speak as often to algorithms as we do to colleagues. In education, especially, our ability to connect, listen and articulate ideas has never been more essential, whether mentoring a student, collaborating across departments or prompting a generative AI tool to support curriculum design. This article explores the new communication landscape: how we interact with each other and how we increasingly interact with machines. As technology becomes more conversational, educators must reconsider what it means to communicate well in both human and digital spaces.

Interpersonal Communication in Education

Interpersonal communication is the backbone of effective teaching and learning. At its core, it is about presence, clarity and empathy. Whether in person or online, educators are expected to communicate in ways that support understanding, encourage inquiry and foster a sense of belonging. In a traditional classroom, much of this is achieved through body language, tone and spontaneous dialogue. However, in hybrid or asynchronous settings, communication becomes more intentional and sometimes more difficult.

The challenge lies in maintaining human connection when interactions are fragmented or mediated by platforms. A written comment on a student’s discussion post carries a different weight than a brief conversation after class. Tone must be interpreted rather than heard. Misunderstandings can occur more easily when nonverbal cues are absent. To overcome these gaps, educators must actively listen, paraphrase for clarity and use inclusive language that respects diverse backgrounds and experiences.

Effective communication in education also includes the ability to give and receive feedback constructively. Students thrive when they receive timely, specific and encouraging feedback. Educators benefit when they create channels for learners to share their thoughts on teaching methods, content delivery and support systems. In both directions, communication builds trust and improves outcomes.

Communicating with Generative AI

The rise of generative AI tools introduces a new form of communication that requires not empathy or emotion, but clarity, intention and iterative thinking. These tools respond to prompts, which are only as effective as the thinking behind them. To engage meaningfully with generative AI, one must learn to frame questions, refine instructions and anticipate multiple responses.

This process mirrors many of the skills involved in teaching. Just as educators learn to adjust their language for different learners, users of generative AI must learn to adapt their prompts based on the tool's limitations and capabilities. A vague prompt often produces ambiguous results. A clear, contextualized prompt yields more useful outputs.

“Communication is about knowing how to ask, how to listen and how to learn, whether your conversation partner is a person or a program”

Students and instructors alike are beginning to recognize that communicating with generative AI is a literacy in its own right. It involves technical understanding and critical thinking. It requires us to think about what we want, how to ask for it and how to evaluate the responses we receive. In many ways, it is a mirror for our reasoning and assumptions.

For educators, this opens new possibilities. AI can support lesson planning, generate feedback, translate content, or simulate classroom dialogue. But these interactions work best when the user communicates precisely and responsibly. Understanding the boundaries of what generative AI can and cannot do is part of the communication process.

What Human and Machine Communication Teach Us

What becomes evident when comparing interpersonal communication and communication with generative AI is that both require attention, reflection and adaptation. The skills used in one domain often enhance the other. Clear communication with colleagues improves clarity when constructing generative AI prompts. Learning how to break down a complex idea for a student prepares us to do the same for a machine.

Just as we teach students to listen actively and ask thoughtful questions in a discussion, we can teach them how to construct purposeful prompts when working with generative AI tools. This kind of parallel instruction builds transferable skills. It encourages learners to slow down, reflect on their needs and articulate their goals, whether they are writing to a peer or an algorithm.

When communicating with people, we are expected to respect boundaries, acknowledge perspectives and build relationships. With generative AI, we must be mindful of data privacy, bias in training data and the implications of relying too heavily on machine-generated insights. The shared lesson here is that good communication is not just effective, it is responsible and thoughtful.

As educators, we are navigating a moment where communication spans both human and machine contexts. Our challenge and opportunity are to model and teach the kind of communication that fosters understanding, inquiry and innovation in both spaces. By embracing this dual responsibility, we help students become not only better learners and collaborators but also better questioners, thinkers and digital citizens.

In the age of generative AI, communication is not just about getting your message across. It is about knowing how to ask, how to listen and how to learn, whether your conversation partner is a person or a program.