  The field of Artificial Intelligence in Education (AIED) focuses on the
intersection of technology, education, and psychology, placing a strong
emphasis on supporting learners' needs with compassion and understanding. The
growing prominence of Large Language Models (LLMs) has led to the development
of scalable solutions within educational settings, including generating
different types of feedback in Intelligent Tutoring Systems. However, the
approach to utilizing these models often involves directly formulating prompts
to solicit specific information, lacking a solid theoretical foundation for
prompt construction and empirical assessments of their impact on learning. This
work advocates careful and caring AIED research by going through previous
research on feedback generation in ITS, with emphasis on the theoretical
frameworks they utilized and the efficacy of the corresponding design in
empirical evaluations, and then suggesting opportunities to apply these
evidence-based principles to the design, experiment, and evaluation phases of
LLM-based feedback generation. The main contributions of this paper include: an
avocation of applying more cautious, theoretically grounded methods in feedback
generation in the era of generative AI; and practical suggestions on theory and
evidence-based feedback design for LLM-powered ITS.
