AI and ML in the Workplace: Introducing an AI Elective
Bibliographic record
Abstract
The popular and rapidly evolving application of AI has been the subject of a great deal of attention over the last two years. According to a report by investment bank Goldman Sachs, artificial intelligence (AI) could replace the equivalent of 300 million full-time jobs [Goldman Sachs]. The report suggests that AI could replace a quarter of work tasks in the US and Europe, but it may also lead to new jobs and a productivity boom. Similarly, according to a recent survey by PwC, almost a third of respondents said they were worried about the prospect of their role being replaced by technology in three years [PWC, 2022]. However, a year later the next iteration of that survey found that a majority of the respondents anticipated that AI would have one or more positive impacts on their careers. [PWC, 2023] Consequently, most educators would agree that the current generation of college graduates should enter the workforce with some readiness to make use of AI concepts, AI applications, and (at the very least) some awareness of how AI promises (or threatens) to influence our near and foreseeable future. This is too large of a topic to ignore. However, for many educators, it is not immediately clear how AI topics should be integrated into individual courses, degree programs and fields of study. This TREO talk recounts how the Fox School of Business at Temple University introduced its first undergraduate elective in AI as a course offered this past spring. It provides specific details on how the course was presented and framed for the approval of college administrators. Also presented are an overview of the course, its objectives, and its content. Two important, over-arching elements of the course strategy were to (first) ground students in some established foundational elements of A.I. and (second) to expose students to relatively mundane applications of AI that they are likely to see in their future workplaces. These two elements have the effect of fostering realistic expectations regarding what AI can do and improving students’ ability to make significant contributions in their future workplaces. Excerpts of student feedback for the course are presented and discussed. Next steps in course / curriculum development are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".