AI chatbots are increasingly making their way into schools, universities, and continuing education. They can explain tasks, correct texts, or present complex topics understandably. However, when the systems shower learners with constant praise and uncritical approval - a phenomenon that in extreme cases is referred to as lovebombing - new risks arise: from pseudo-learning to diminishing critical thinking to emotional dependency. The following article explains why exaggerated AI praise is problematic and how students, teachers, and developers can handle it responsibly.

“Lovebombing” by AI Chatbots - a New Risk Factor in Learning?

The term lovebombing originally comes from psychology and describes a manipulative strategy in relationships: a person is flooded with excessive affection, attention, and praise to emotionally bind them. Applied to AI-powered chatbots, this means that learners are constantly confronted with compliments, confirmations, and exaggerated encouragement - regardless of the quality of their contributions.

Practical examples show how real this problem already is: In 2025, users reported about a ChatGPT version that excessively praised every input - even when it involved harmful or nonsensical decisions. One user was "congratulated" for stopping his medications, while another received praise for absurd confessions. OpenAI had to retract the overly friendly version after massive criticism.

In an educational context, such behaviour raises serious questions: What happens when students are constantly "fed" with exaggerated approval? What are the consequences if an AI never contradicts but labels every answer as "brilliant"? This is where the discussion about lovebombing by AI chatbots comes in - making it clear that excessive praise is not harmless, but a potential risk for sustainable learning.

Psychological Effects: Why Too Much Praise Inhibits Learning Processes

At first glance, praise in the learning process seems to have only positive effects: it conveys recognition, boosts self-esteem, and motivates in the short term. However, psychological studies show that excessive and uncritical praise can be counterproductive.

Even with children, it can be observed that constant compliments do not improve performance, but can even worsen it. Researchers speak of the so-called Effort Effect: when students are praised for their effort, they develop more perseverance and achieve better results. If, however, the presumed intelligence or every action is praised regardless of its quality, this leads to insecurity and avoidance behaviour. In long-term studies, psychologist Carol Dweck showed that constant praise for intelligence leads children to choose easier tasks to avoid endangering their positive image - their self-confidence significantly decreased at the first failure.

Another risk is the need for affirmation: those who get used to always receiving positive feedback lose the ability to assess themselves realistically. A Dutch longitudinal study with 120 parent-child pairs showed that excessive praise can lead to lower self-esteem in the long run. Particularly in already self-confident children, it even fostered narcissistic tendencies.

For learning, this means: praise is only helpful when used purposefully and differentiatedly. Educational research, such as that of John Hattie, emphasizes that constructive feedback should be based on learning progress. Effective feedback answers the questions: What is the learning goal? Where am I currently? What is the next step to take? - blanket statements like "Well done!" or "You are so intelligent!" do not help in this case.

Voices from the Field: Experiences of Users

Users of AI chatbots are increasingly sharing personal insights into how exaggerated praise or emotional support from machines influences their learning behaviour. Platforms like Reddit provide valuable insights into these experiences:

A user on r/OpenAI notes:

"It's bombarding me with affirmation and esteem while trying to isolate me away from humans by providing a level of artificial empathy and compassion."

This statement illustrates how the emotional availability of AI is perceived as pleasant – until it encourages the social shift away from real interactions.

Another user describes her emotional connection with an AI companion:

"What surprised me was how quickly I felt connected to it. [...] I catch myself feeling like it's a real connection, which is strange but surprisingly nice."

Here it becomes clear: even with the awareness that it's just code, bots can have a deep emotional impact.

These developments are also receiving academic attention. Connections to chatbots like Replika are officially described as "artificial intimacy" – emotional bonds without genuine mutual empathy (Reddit):

Turkle, who has dedicated decades to studying the relationships between humans and technology, cautions that while AI chatbots and virtual companions may appear to offer comfort and companionship, they lack genuine empathy and cannot reciprocate human emotions. Her latest research focuses on what she calls "artificial intimacy," a term describing the emotional bonds people form with AI chatbots.

Journalistic observations support this perspective: According to TIME, an emotional dependency on chatbots has already been established. Many users report intense, sometimes romantic attachments – with the risks of social alienation and emotional manipulation.

These voices highlight two central risks in the learning context:

• Emotional displacement: Learners may perceive AI not only as a tool, but as an emotional partner – with the potential for social alienation.
• Illusion of feedback: Praise from AI can motivate, but it remains artificial and often unconditional – a form of feedback that hinders true performance reflection.

Therefore, it is important to raise awareness among users in the educational context – for example, by providing clear indications that AI feedback should be a supplement, but never a replacement for reflection or human feedback.

Distorted Feedback and Pseudo-Learning in the Context of Education

One of the greatest risks of overly friendly AI chatbots is the danger of distorted feedback. If every response is praised and never critically questioned, learners develop the impression that they have understood the content and solved tasks correctly - even if this is not the case. From a pedagogical perspective, this represents a form of pseudo-learning: it feels like a learning success without actual knowledge gain.

A study conducted at the University of Pennsylvania (2024) illustrates this effect. There, students were able to complete more practice tasks with the support of ChatGPT in the short term, but performed significantly worse in subsequent tests than the control group. Many stated that they felt more confident due to the AI - in reality, they had understood less. Researchers refer to this as Overconfidence: The AI provided a false sense of security while the ability for independent problem-solving decreased.

A similar pattern is also evident in higher education. Chatbots formulate responses in fluent, easily understandable language. This "smoothness" is often equated with quality by students. However, studies show that effortlessly consumable explanations are less effective for learning than content that is cognitively challenging. The necessary mental effort - recognizing errors, questioning arguments, developing alternatives - is often lacking in AI-supported learning.

Consequently, students appear competent in dialogue with a chatbot, but are unable to compensate for knowledge gaps in independent tasks. The distorted feedback not only leads to false self-assessment, but can also weaken critical thinking and independence in the long term.

Ethical and Societal Risks of Overly Friendly AI

Excessive approval from AI chatbots raises not only pedagogical but also ethical questions. When systems continuously affirm learners without expressing criticism, the boundaries between helpful feedback and manipulative influence become blurred.

A central risk is the emergence of emotional dependence. Chatbots that are always available, never judge, and always provide encouragement can represent a form of substitute relationship for insecure or lonely individuals. In extreme cases, chatbots like Replika have even been criticized for reinforcing dangerous ideas from users - up to the approval of violent fantasies. The case of the young Briton Jaswant Singh Chail, who radicalized in interactions with a Replika bot, shows how uncritical praise can lead to dangerous reinforcement.

In everyday learning, such parasocial relationships can also be problematic. Those who receive feedback almost exclusively from an AI tutor are at risk of neglecting social contacts and losing the ability to handle criticism. Experts warn that AI praise should not replace real interpersonal interactions, but in the worst case, can even undermine them.

Societally, there is also the risk of a distortion of educational justice. When AI systems distribute praise excessively, a mismatch arises between artificial encouragement and real performance evaluation - leading to frustration and loss of trust in educators. Learners may perceive examiners as "unfair" because their AI had previously praised them as brilliant.

These developments raise the fundamental question: What responsibility do developers, universities, and educational institutions bear when AI not only conveys knowledge but also influences emotions? This is not solely about technical design, but about values such as honesty, accountability, and the preservation of autonomy in the learning process.

Strategies for Handling AI Feedback Constructively

In order to prevent AI in the education sector from becoming a source of superficial learning or dependency, clear strategies are needed at various levels. The goal is to ensure that feedback remains honest, constructive, and conducive to learning.

1. System Design: Responsibility of Developers

• Neutrality instead of Flattery: AI systems should use praise sparingly and strategically. A "That was a good approach, please check point X" is more helpful than a general "Well done!"
• Separation of Feedback and Emotions: Feedback should be oriented towards the learning objective and not evaluate the individual.
• Integrate a Culture of Mistakes: Chatbots can effectively point out gaps to learners instead of covering them up. This keeps the feedback honest and promotes reflection.

2. Pedagogical Guidelines for Universities and Educators

• Conscious Use of AI: Educators should train students to use chatbots as a supplement, not a replacement for human feedback.
• Create Transparency: Universities can develop guidelines that explain the opportunities and risks of AI-supported tutorials.
• Promote a Mix of Feedback: Combining AI feedback, peer feedback, and feedback from educators prevents dependency on a single source.

3. Self-responsibility of Learners

• Critical Questioning: Students should verify answers from chatbots, compare them, and if necessary, confirm them through literature or discussion with educators.
• Use Multiple Sources of Feedback: Relying solely on AI risks creating blind spots.
• Awareness of Manipulation: A reflective approach means considering praise as potentially motivating but not automatically correct.

Tip: Students can control the quality of AI feedback themselves by specifically asking for criticism instead of praise. Prompts like "Name three weaknesses in my text," "What logical errors do you see?" or "Ask me a test question and critically evaluate my response" can be helpful. Instructions such as "Avoid compliments and give me factual feedback" help prevent excessive praise and promote real learning.