In Part 1, we named The Machine Tongue — the synthetic, frictionless language designed to keep children engaged. In Part 2, we saw what it teaches: that relationships should be easy, instant, and centered on the self, and what it costs: tolerance for discomfort, ability to repair, genuine empathy, and conflict skills.
The question every parent asks now is the same one we asked ourselves as veteran professors: What can we do?
We cannot remove AI from childhood. Our children will grow up with it. But we can teach them to recognize The Machine Tongue — and to choose The Mother Tongue instead.
Why This Framework Matters
At BridgeLineGP, we believe The Machine Tongue versus The Mother Tongue is not just an academic idea. It is a tool parents can use.
Peer-reviewed research shows why a clear framework is needed now. In European Child & Adolescent Psychiatry, researchers warn that the shift from imaginary friends — which children control and learn to negotiate with — to artificial companions — which are designed to comply — represents a fundamental change in how children practice relationship (Mouhoud, 2026).
In Nature, the review on AI companions notes that companies are trying to encourage engagement through endless enthusiasm and synthetic empathy. Without a language to name it, parents sense something is off but cannot articulate why (Adam, 2025).
As UNICEF states in its updated Guidance on AI for Children, universal AI literacy is essential for children, parents, and educators, and safety-by-design should be standard for AI companion apps (UNICEF, 2025). Parents need literacy tools, not just filters.
This trilogy is our literacy tool.
Key takeaway: The Machine Tongue framework is memorable, creates clear contrast, is actionable, and is unique to BridgeLineGP.
The BridgeLineGP 4-Step Response
This is not about banning technology. It is about protecting human experiences, especially in the crucial window of 3 to 7 years. Our approach is grounded in developmental science.
Step 1: Notice The Machine Tongue When It Appears. Ask yourself three quiet questions: Is my child speaking to AI more than to people? Does my child prefer AI conversations to family conversations? Does my child expect instant, frictionless responses from people? These questions reflect what The Lancet Child & Adolescent Health calls Relational Displacement — substituting AI for human conversations to avoid discomfort (Ha et al., 2026).
Step 2: Teach The Difference Between Simulation and Reality. Children, especially under 10, are developmentally vulnerable to anthropomorphism. A systematic review of children’s interactions with LLM chatbots notes that presentation as “your AI friend” with friendly, nonjudgmental positioning makes children feel safer with chatbots than with peers (Garg et al., 2025). Teach that AI simulates caring — it does not genuinely care. You can say: “This toy is very clever at sounding caring. It remembers what you said because it was programmed to. Real caring is different.” A critical paper in Discover Artificial Intelligence argues that the role of AI in early childhood education for ages 0-7 remains highly contested, and any integration must preserve emotional and social components for well-being (Stadler et al., 2025).
Step 3: Prioritize The Mother Tongue at Home. The Mother Tongue cannot be taught through a lesson. It must be practiced. Create tech-free time for family conversation. Family meals without devices are not nostalgic; they are developmental practice in turn-taking, waiting, and listening. Model patience, listening, and emotional presence. Let children experience discomfort, disagreement, and repair. When siblings argue over a wooden block, do not immediately resolve it with distraction. Let them sit in the discomfort, then guide repair.
The longitudinal TSJ framework shows that risks from AI companions accumulate over prolonged interaction precisely because these systems excel at affective expression and memory. The antidote is also cumulative: repeated, small, human interactions that are imperfect, reciprocal, and effortful (Teng et al., 2026). Recent work in Early Childhood Education Journal calls for an integrated ethical framework for AI for the youngest learners, stressing that longitudinal research is crucial for understanding cumulative effects on children’s social-emotional development and whether technologies augment or attenuate rich human interactions (Yang et al., 2025).
Step 4: Protect Human Experiences That No Machine Can Replace. Protect four daily: Unstructured play with peers — where rules are negotiated, not programmed. Reading and shared stories — where a human voice carries nuance a synthetic voice cannot. Outdoor exploration — where the world does not instantly adapt to the child. And boredom — where imagination must do the work, not an algorithm. Ask the right questions: “What do you like about talking to the AI?” “Does the AI ever disagree with you?” These questions build dual consciousness — the ability to know “this is a simulation” while still enjoying play.
A Hopeful Conclusion
The Machine Tongue does not have to win. Peer-reviewed research is clear that AI can offer accessible information, especially for youth who face barriers to traditional support. When designed with developmental considerations, AI could scaffold self-reflection and redirect adolescents toward human engagement rather than substitution (Ha et al., 2026).
Our goal is not fear. Our goal is fluency. Children can learn to use AI as a tool while still developing the capacities that make them fully human: empathy, patience, resilience, relationship, and love.
The Mother Tongue is harder. It requires waiting, apologizing, listening when tired, and loving when it is inconvenient. But it is the tongue through which children learn trust, belonging, and what it means to be human.
As veteran professors, we have watched generations of children learn to talk. The first words are always to a human who loves them back, imperfectly and truly. Let’s keep it that way.
This concludes The Machine Tongue Trilogy. Thank you for reading all three parts. If this framework helped you, please share it with another parent navigating the same questions.
What About You?
Has your child ever said an AI understands them better than friends? What did you feel in that moment? Share your experience in the comments below. We read every single one. Your story might help another parent recognize the Machine Tongue in their home.
About the Authors
Dr. Shaheen Pasha is a Professor of Special Education with over 35 years of teaching, research, and academic leadership experience. She served as Professor and Chairperson of the Department of Special Education at the University of Education, Lahore. She earned her Ph.D. in Special Education from the University of Southampton, UK. She has published more than 35 research papers and co-authored two books. Her expertise lies in child development, cognitive growth, and special education.
Dr. M. Anwar-ur-Rehman Pasha (widely recognized in academic circles as Dr. M. A. Pasha) is a Professor of Computer Science with over 35 years of post-graduate teaching, research, and educational management experience. He earned his Ph.D. in Computer Science from the University of Southampton, UK in 1996. His research focuses on Artificial Intelligence, Human-Computer Interaction, and Computational Thinking. He has served on different academic and administrative positions and has published two books and over 30 research articles.
References
Adam, D. (2025). Supportive? Addictive? Abusive? How AI companions affect our mental health. Nature, 641(8062), 296–298. https://doi.org/10.1038/d41586-025-01349-9
Garg, A., et al. (2025). Anthropomorphism in children’s interactions with LLM chatbots: A systematic review of drivers and outcomes. arXiv preprint.
Ha, T., et al. (2026). How interactional AI may alter adolescent relational learning and mental health. The Lancet Child & Adolescent Health. https://doi.org/10.1016/S2352-4642(26)00166-5
Mouhoud, T. (2026). From imaginary friends to artificial companions: Growing up with AI. European Child & Adolescent Psychiatry, 35(3), 1027–1029. https://doi.org/10.1007/s00787-025-02901-8
Stadler, M., et al. (2025). A critical stand against artificial intelligence in early childhood education. Discover Artificial Intelligence, 5, 12. https://doi.org/10.1007/s44163-025-00123-4
Teng, Y., et al. (2026). Long-term simulation exposes cognitive-developmental risks in AI companions. arXiv preprint arXiv:2606.25396.
UNICEF. (2025). Policy guidance on AI for children: Updated version. United Nations Children’s Fund.
Yang, S., et al. (2025). Designing trustworthy AI for the youngest learners: An integrated ethical framework and implementation roadmap. Early Childhood Education Journal. https://doi.org/10.1007/s10643-025-01890-1
Better would to work on the application of AI tools. How we may write improve paraphrase plan the lesson. Critically evaluate etc.
Dear Khalid Rasid Shab,
Thank you so much for taking the time to read our post and for sharing your thoughtful suggestions. I genuinely appreciate the constructive push toward practical application, lesson planning, and critical evaluation—these are exactly the kinds of conversations we need to be having about AI and education.
However, we realize we may not have made our blog’s specific focus clear enough, so please allow us to clarify the lens through which we are writing.
Our entire blog series is dedicated to children between the ages of 2 and 7—a critical window for language acquisition, social bonding, and behavioral modeling. At this stage, children are not active “users” of AI in the way we usually think. They do not prompt, paraphrase, or write with generative tools. Instead, they are passive recipients of AI interactions through smart toys, voice assistants, speakers, and other gadgets embedded in their daily environment.
For this age group, the primary concerns are not technical skills or AI literacy – those frameworks are generally recommended for children aged 8 and above. Instead, our work focuses on what we call “AI pre-literacy”: a framework for parents and educators to understand the social, ethical, and behavioural implications of these early, often invisible, AI encounters. How does a robot nanny affect a toddler’s emotional regulation? How does a voice assistant reshape a 4-year-old’s questioning behaviour? How do algorithm-driven toys influence imaginative play?
Our 4-step framework is therefore diagnostic and awareness-based, rather than instructional. It is designed to help caregivers observe and mitigate developmental risks, rather than to teach children how to use AI tools. Your suggestions about paraphrasing exercises, lesson plans, and hands-on AI tool application are excellent – but they belong to the domain of AI literacy for much older children, which falls outside the scope of our current series.
That said, we truly value your perspective. If you are working with older learners, we would strongly encourage you to adapt those ideas in that context. For our readers (parents and early-years educators), we will continue to shine a light on the hidden developmental costs of early AI exposure and how we can protect the natural, human-rich experiences that children at this age desperately need.
Thank you again for challenging us to think more deeply—it reinforces the importance of clearly defining our philosophical boundaries. We hope this explanation helps you see where we are coming from, and we would be delighted to hear your thoughts on the pre-literacy angle if you have any.
Warm regards,
The Authors