Skip to main content
Social Sci LibreTexts

10.4: What can be done about it?

  • Page ID
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    I want to end this post – and this series – on a hopeful note. There’s no denying that Generative AI and related technologies have the potential to positively impact the education system. The same could easily be said about any technology, from writing tools to word processors, PCs to smartphones. The issue – as with all of these technologies – is not whether they can improve learning, but how they are used.

    If all we use AI for is efficiency, then we’re heading towards EdTech v2.0. Over the past couple of decades we’ve seen wave after wave of technology that promised enormous gains to learning, but delivered very little. We’ve heard the phrase “this will revolutionise education” again and again, and in general the education system – and its flaws – has proven to be extremely robust. We are now starting to see the potential negative impact of AI technologies, including from datafication, predictive profiling, and the potential for generative AI to perpetuate bias.

    So, to counteract the entrenchment of existing power structures, and the centralising of wealth in the hands of the already wealthy, seems like a huge challenge and not something that can be tackled by educators.

    But Artificial Intelligence isn’t EdTech. It’s not an app or a piece of equipment, or even a single system. It is a complex infrastructure that will ultimately be woven through all of the technologies we already use, and those on the horizon. And importantly, it’s not quite fully established in education yet, which gives us an opportunity for critique. This series of posts aimed to support that critique by engaging students and educators across different curriculum areas in meaningful discussions about AI and the future of education.

    Here are five final practical ideas for Teaching AI Ethics:

    10.4: What can be done about it? is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by LibreTexts.

    • Was this article helpful?