  Many educational technologies use artificial intelligence (AI) that presents
generated or produced language to the learner. We contend that all language,
including all AI communication, encodes information about the identity of the
human or humans who contributed to crafting the language. With AI
communication, however, the user may index identity information that does not
match the source. This can lead to representational harms if language
associated with one cultural group is presented as "standard" or "neutral", if
the language advantages one group over another, or if the language reinforces
negative stereotypes. In this work, we discuss a case study using a Visual
Question Generation (VQG) task involving gathering crowdsourced data from
targeted demographic groups. Generated questions will be presented to human
evaluators to understand how they index the identity behind the language,
whether and how they perceive any representational harms, and how they would
ideally address any such harms caused by AI communication. We reflect on the
educational applications of this work as well as the implications for equality,
diversity, and inclusion (EDI).
