  As AI technology continues to advance, the importance of human-AI
collaboration becomes increasingly evident, with numerous studies exploring its
potential in various fields. One vital field is data science, including feature
engineering (FE), where both human ingenuity and AI capabilities play pivotal
roles. Despite the existence of AI-generated recommendations for FE, there
remains a limited understanding of how to effectively integrate and utilize
humans' and AI's knowledge. To address this gap, we design a readily-usable
prototype, human\&AI-assisted FE in Jupyter notebooks. It harnesses the
strengths of humans and AI to provide feature suggestions to users, seamlessly
integrating these recommendations into practical workflows. Using the prototype
as a research probe, we conducted an exploratory study to gain valuable
insights into data science practitioners' perceptions, usage patterns, and
their potential needs when presented with feature suggestions from both humans
and AI. Through qualitative analysis, we discovered that the Creator of the
feature (i.e., AI or human) significantly influences users' feature selection,
and the semantic clarity of the suggested feature greatly impacts its adoption
rate. Furthermore, our findings indicate that users perceive both differences
and complementarity between features generated by humans and those generated by
AI. Lastly, based on our study results, we derived a set of design
recommendations for future human&AI FE design. Our findings show the
collaborative potential between humans and AI in the field of FE.
