The field of human-AI collaboration is moving towards a deeper understanding of the complex relationships between humans and artificial intelligence systems. Researchers are exploring the boundaries of trust, knowledge transfer, and cooperation between humans and AI, with a focus on developing more effective and efficient collaboration frameworks. A key area of innovation is the development of models that can explain their reasoning and decisions in ways that humans can understand, facilitating more effective knowledge transfer and trust-building. Another important direction is the investigation of trust dynamics in human-AI interactions, including the impact of trust repair strategies and the development of optimal policies for facilitating real-time adjustment of human trust in autonomous systems. Noteworthy papers in this area include:

• A Path to Loving, which lays the foundations for a rigorous ontological characterization of love and its relevance to AI-based applications.
• When Models Know More Than They Can Explain, which introduces a framework for evaluating knowledge transfer in human-AI collaboration and highlights the need for dedicated optimization of model explanations.
• Understanding Human-AI Trust in Education, which proposes a distinct form of trust with AI chatbots that differs from human-human and human-technology models of trust.
• AI Tutors vs. Tenacious Myths, which demonstrates the effectiveness of personalized AI dialogue in correcting stubborn misconceptions in psychology and education.
• Modeling Trust Dynamics in Robot-Assisted Delivery, which investigates the impact of trust repair strategies on human trust in autonomous systems and develops a model for capturing the dynamics of trust and human action probabilities.