AI-GenAI
Teachers and Parents Can’t See AI Chat Transcripts While Gemini may be “student safe,” only administrators can review chat histories. That’s a huge blind spot. If a student is confused by a Gemini response, misuses the tool, or gets inaccurate information—teachers and parents won’t know unless the student says something.
Is AI doing the thinking—or the student? Many features encourage speed and convenience, but could inadvertently promote over-reliance. Students can get summaries, answers, and explanations so easily that critical thinking risks taking a backseat.
There’s no way to track edits or usage Gemini doesn’t offer version history for AI-generated content. That means teachers can’t see how a document evolved—or how much of it came from AI.
Equity gaps may widen Some schools have tech coaches, training time, and infrastructure to support thoughtful AI use. Others don’t. Without equitable implementation support, Gemini’s benefits may be limited to already well-resourced districts.
The term “ethicswashing” was recently coined to describe the phenomenon of instrumentalising ethics by misleading communication, creating the impression of ethical Artificial Intelligence (AI), while no substantive ethical theory, argument, or application is in place or ethicists involved. Ethicswashing resembles greenwashing for environmental issues and has become an issue – particularly since 2019 with Thomas Metzinger’s harsh criticisms as a member of the EU panel for developing ethical guidelines for AI, which he called “ethicswashing.” Nowadays, increased ethics washing has changed the perception of AI ethics, leading critics to find a “trivialization” of ethics that may even lead to “ethics bashing.”
Considering the scattered literature body and the various manifestations of digital ethicswashing, we recognise the need to assess the existing literature comprehensively. To fill this gap, this research systematically reviews current knowledge about digital ethicswashing stemming from various academic disciplines, contributing to an up-to-date assessment of its underlying characteristics. Applying content analysis to map the field leads us to present five thematic clusters: ethicswashing, ethics bashing, policymaking and regulation, watchdogs, and academia.
In conclusion, we synthesise ethicswashing along a process-perception-outcome framework to provide future research to explore the multiple meanings of digital ethicswashing.