There is a need to conceptualize a multidimensional taxonomy for learner-AI interaction. This conceptual/perspective article shares recent work on AI learner education and further presents new conceptions for a multidimensional taxonomy for learner-AI interaction. A review of the literature is conducted (N = 11). Open coding is used to summarize an overview of work, challenges, and findings reported. The summarized work is then used to conceptualize considerations for a multidimensional taxonomy for learner-AI interaction. The contribution of this work is in identifying unforeseen limitations in characterizing human-AI interaction and presenting new conceptions for a multidimensional taxonomy for learner-AI interaction based on the synthesis of the reviewed literature. This work thus shares current findings and challenges reported by the literature and our conceptions. Four conceptions are introduced, namely the alignment between the learner and AI; diverse metrics for the learner, AI, and learner-AI interaction; feedback direction when summarizing interactions; and what works in human-AI interaction by using prior research. We find there to be challenges with the use of AI by humans. The more interaction time spent between humans and AI may not necessarily lead to enhanced learning and understanding. Humans may exploit and use AI in inappropriate ways such as plagiarism. This eminent threat begs the question to reconsider our evaluation methods in light of AI systems.