Tutor-Tested Strategies
AI is transforming life and work across professions, including education. Despite fears of academic dishonesty, plagiarism, and bias, many educators have now taken the plunge into exploring AI-enhanced curriculum design and instruction. Educators are drawn to AI for its ability to quickly generate standards-aligned content that reflects instructional objectives and teacher expertise through well-crafted prompts. Tools can help combat burnout by streamlining lesson preparation and personalizing learning. Customizable AI rooms and learning tools take these benefits even further, enhancing both group and individual instruction, including tutoring.
While AI tutors can provide personalized feedback, they cannot yet replicate what human tutors do best: connect, empathize, and build trust. AI can simulate dialogue, but it lacks emotional understanding. Human tutors perceive tone, hesitation, and body language, nonverbal cues that reveal engagement and comprehension. They also navigate ethical and cultural complexities, exercising moral judgment that AI simply doesn't possess.
As eLearning continues to evolve, many educators worry that AI might replace the human element of teaching. But rather than threatening tutors' roles, AI can actually enhance them. By integrating Large Language Models (LLMs) into live tutoring, educators can boost efficiency, personalize learning, and enrich engagement, while keeping the human relationship at the center. Below are ten practical, human-centered ways to integrate LLMs into your tutoring sessions.
1. Vocabulary Development
Live tutoring sessions naturally introduce new vocabulary through reading, writing, speaking, and listening. Maintaining a shared digital "vocabulary notebook" is a great practice, but creating review activities can be time-consuming. Use an LLM to generate multiple-choice questions, matching tasks, gap-fills, or word-bank activities based on session vocabulary. Always review the AI's work for accuracy and adjust difficulty to match each learner.
2. Re-Levelled Texts
When a reading passage proves too challenging, or not challenging enough, LLMs can quickly adjust text complexity. Upload or paste the passage and prompt the model to adapt it to a target level. This allows tutors to meet students where they are in real time without losing valuable minutes rewriting text manually.
3. Video Comprehension
Videos enhance listening skills and provide content knowledge across subjects. Copy and paste a video transcript (e.g., from YouTube) into an LLM to generate comprehension questions. Ask for timestamped answer keys so students can locate information in the video while checking answers. This helps strengthen both listening comprehension and digital literacy.
4. Speaking And Writing Prompts
Generate speaking or writing prompts tailored to a student's level, target grammar, and learning goals. Request three options so the student can choose the one they prefer, promoting autonomy and engagement. Example: "Create three discussion prompts for a B2-level student about renewable energy, each using the present perfect tense."
5. Multiple Genres
To balance expository reading, use AI to produce creative texts such as short stories, poems, songs, or anecdotes on the same topic. Shifting genres can reenergize learners and make content memorable. For example, after studying the Byzantine Empire, ask for a short rhyme or humorous dialogue on the same theme, then invite the student to write their own version.
6. Student-As-Main-Character Passages
Personalized stories build motivation. Provide details about your student's interests, weekend activities, or hobbies, and prompt an LLM to craft a short story featuring them as the protagonist. Add parameters for word count and difficulty level, then ask the model to create comprehension questions. Always proofread outputs for factual accuracy, balanced answer choices, and clarity.
7. Multiple Voices And Safe Debate
Tutors bring unique experiences to sessions, but AI can introduce diverse perspectives safely. Use persona prompts (e.g., "Act as a historian supporting…") to simulate multiple viewpoints in a discussion or debate. Screen-share AI responses so students can analyze tone and argument quality. This approach strengthens critical thinking and respectful discourse, especially in sensitive topics.
8. Real-Time Inquiry
Curious students often ask spontaneous questions. If a topic falls outside your immediate expertise, use AI to scaffold inquiry collaboratively. Enter the student's question into an LLM and examine the response together, evaluating credibility, summarizing key points, and identifying sources. With younger learners, use age-appropriate tools for safer exploration.
9. Language-Based Image Prompts
Some LLMs now include image generation. Leverage this for descriptive language practice. Create a prompt in the target language to produce an image, then ask the student to describe what they see. Compare their description with the original prompt to discuss similarities, differences, and vocabulary use. Allow students to write their own prompts and analyze the AI's interpretations for added fun and feedback.
10. Progress Reporting
Keeping students informed about their progress strengthens motivation and accountability. Instead of drafting every message from scratch, use an LLM to turn session notes into concise progress updates. Specify tone, such as encouraging, professional, or friendly, and include two positives for every area of improvement. Always personalize and proofread before sending.
The Human Advantage
These ten strategies illustrate how AI can amplify human teaching, not replace it. Tutors remain the bridge between data and empathy, the interpreters of emotion, ethics, and curiosity that AI cannot replicate. As LLMs handle routine preparation, educators can devote more energy to what matters most: building relationships, understanding learners, and nurturing growth. AI can make tutoring more efficient, personalized, and creative, but the heart of tutoring will always be human.
