Abstract

Generative artificial intelligence (AI) tools such as ChatGPT, Bard, and Claude have recently become a concern in the delivery of engineering education. For courses focused on computer coding, such tools are capable for creating working computer code across a range of computer languages and computing platforms. In a course for mechanical engineers focused on C++ coding for the Arduino microcontroller and coding engineering problems in Matlab, a new approach to assessment of programing homework assignments was developed. This assessment moved the focus of assigned points from the correctness of the code to the effort and understanding of the code demonstrated by the student during in-person grading. Students who participated fully in in-person grading did significantly better on a midterm exam. Relative to a previous semester, where grading was focused on correct code, students had a slightly higher average midterm exam score. This approach appears to be effective in supporting computational learning in the face of evolving tools that could be used to circumvent learning. Future work should examine how to also encourage responsible use of generative AI in computational learning.

References

1.
Hawley
,
M.
,
2023
, “
The Complete Generative AI Timeline: History, Present and Future Outlook
,” CMSWire.com, San Francisco, CA, accessed July 31, 2023, https://www.cmswire.com/digital-experience/generative-ai-timeline-9-decades-of-notable-milestones/
2.
Lim
,
W. M.
,
Gunasekara
,
A.
,
Pallant
,
J. L.
,
Pallant
,
J. I.
, and
Pechenkina
,
E.
,
2023
, “
Generative AI and the Future of Education: Ragnarök or Reformation? A Paradoxical Perspective From Management Educators
,”
Int. J. Manage. Educ.
,
21
(
2
), p.
100790
.10.1016/j.ijme.2023.100790
3.
Goodfellow
,
I. J.
,
Pouget-Abadie
,
J.
,
Mirza
,
M.
,
Xu
,
B.
,
Warde-Farley
,
D.
,
Ozair
,
S.
,
Courville
,
A.
, and
Bengio
,
Y.
,
2014
, “
Generative Adversarial Nets
,”
Proceedings of the 27th International Conference on Neural Information Processing Systems
, MIT Press, Cambridge, MA, Vol. 2, pp. 2672–2680.https://proceedings.neurips.cc/paper_files/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
4.
Vaswani
,
A.
,
Shazeer
,
N.
,
Parmar
,
N.
,
Uszkoreit
,
J.
,
Jones
,
L.
,
Gomez
,
A. N.
,
Kaiser
,
Ł.
, and
Polosukhin
,
I.
,
2017
, “
Attention is All You Need
,” I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, eds.,
Advances in Neural Information Processing Systems
, Vol. 30, Curran Associates, Inc., New York.https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
5.
Radford
,
A.
,
Narasimhan
,
K.
,
Salimans
,
T.
, and
Sutskever
,
I.
,
2018
, “
Improving Language Understanding by Generative Pre-Training
,”
OpenAI
, San Francisco, CA, accessed July 31, 2023, https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
6.
Radford
,
A.
,
Wu
,
J.
,
Child
,
R.
,
Luan
,
D.
,
Amodei
,
D.
, and
Sutskever
,
I.
,
2018
, “
Language Models Are Unsupervised Multitask Learners
,”
OpenAI
, San Francisco, CA, accessed July 31, 2023, https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf
7.
Devlin
,
J.
,
Chang
,
M.-W.
,
Lee
,
K.
, and
Toutanova
,
K.
,
2019
, “
BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding
,”
NAACL-HLT
, Minneapolis, MN, June 2–7, pp.
4171
4186
.https://aclanthology.org/N19-1423.pdf
8.
Casey
,
M.
,
2023
, “
Large Language Models: Their History, Capabilities and Limitations
,”
Snorkel AI
, Redwood City, CA, accessed July 31, 2023, https://snorkel.ai/large-language-models-llms/
9.
Li
,
Y.
,
Choi
,
D.
,
Chung
,
J.
,
Kushman
,
N.
,
Schrittwieser
,
J.
,
Leblond
,
R.
,
Eccles
,
T.
,
Keeling
,
J.
,
Gimeno
,
F.
,
Lago
,
A. D.
,
Hubert
,
T.
,
Choy
,
P.
,
d'Autume
,
C. D. M.
,
Babuschkin
,
I.
,
Chen
,
X.
,
Huang
,
P.-S.
,
Welbl
,
J.
,
Gowal
,
S.
,
Cherepanov
,
A.
,
Molloy
,
J.
,
Mankowitz
,
D. J.
,
Robson
,
E. S.
,
Kohli
,
P.
,
Freitas
,
N.
,
de
,
Kavukcuoglu
,
K.
, and
Vinyals
,
O.
,
2022
, “
Competition-Level Code Generation With AlphaCode
,”
Science
,
378
(
6624
), pp.
1092
1097
.10.1126/science.abq1158
10.
Welsh
,
M.
,
2023
, “
The End of Programming
,”
Commun. ACM
,
66
(
1
), pp.
34
35
.10.1145/3570220
11.
de Kereki
,
I. F.
,
2017
, “
Detecting Academic Misconduct in Introductory Computer Science Courses
,”
Plagiarism Across Europe and Beyond 2017 Conference Proceedings
, Brno, Czech Republic, pp.
45
58
.https://academicintegrity.eu/conference/proceedings/2017/Kereki_Detecting.pdf
12.
Adorjan
,
A.
, and
de Kereki
,
I. F.
,
2017
, “
Academic Misconduct in Projects: Perspective of Students and Teachers of Introductory Computer Science Courses
,”
XLIII Latin American Computer Conference (CLEI)
, Cordoba, Argentina, Sept. 4–8, pp.
1
7
.10.1109/CLEI.2017.8226375
13.
Basu
,
D.
, and
Ramaprasad
,
H.
,
2023
, “
Design and Evaluation of an Academic Integrity Module for Computer Science Students
,”
Proc. of the 2023 ASEE Annual Conference and Exposition
, Baltimore, MD, June.https://nemo.asee.org/public/conferences/327/papers/37831/view
14.
Sheard
,
J.
,
Dick
,
M.
,
Markham
,
S.
,
Macdonald
,
I.
, and
Walsh
,
M.
,
2002
, “
Cheating and Plagiarism: Perceptions and Practices of First Year IT Students
,”
Proceedings of the Seventh Annual Conference on Innovation and Technology in Computer Science Education
,
Association for Computing Machinery
,
New York
, pp.
183
187
.10.1145/544414.544468
15.
Bundy
,
A.
,
2007
, “
Computational Thinking is Pervasive
,”
J. Sci. Practical Comput.
,
1
(
2
), pp.
67
69
.https://core.ac.uk/download/pdf/28961399.pdf
16.
Lu
,
J. J.
, and
Fletcher
,
G. H. L.
,
2009
, “
Thinking About Computational Thinking
,”
Proceedings of the 40th ACM Technical Symposium on Computer Science Education
,
Association for Computing Machinery
,
New York
, Chattanooga, TN, Mar. 4–7, pp.
260
264
.10.1145/1539024.1508959
17.
Fisher
,
M.
,
Goddu
,
M. K.
, and
Keil
,
F. C.
,
2015
, “
Searching for Explanations: How the Internet Inflates Estimates of Internal Knowledge
,”
J. Exp. Psychol. Gen.
,
144
(
3
), pp.
674
687
.10.1037/xge0000070
18.
Wing
,
J. M.
,
2006
, “
Computational Thinking
,”
Commun. ACM
,
49
(
3
), pp.
33
35
.10.1145/1118178.1118215
19.
Shute
,
V. J.
,
Sun
,
C.
, and
Asbell-Clarke
,
J.
,
2017
, “
Demystifying Computational Thinking
,”
Educ. Res. Rev.
,
22
, pp.
142
158
.10.1016/j.edurev.2017.09.003
20.
Denning
,
P. J.
,
2009
, “
The Profession of ITBeyond Computational Thinking
,”
Commun. ACM
,
52
(
6
), pp.
28
30
.10.1145/1516046.1516054
21.
Peteranetz
,
M. S.
,
Flanigan
,
A. E.
,
Shell
,
D. F.
, and
Soh
,
L.-K.
,
2017
, “
Computational Creativity Exercises: An Avenue for Promoting Learning in Computer Science
,”
IEEE Trans. Educ.
,
60
(
4
), pp.
305
313
.10.1109/TE.2017.2705152
22.
Peteranetz
,
M. S.
,
Flanigan
,
A. E.
,
Shell
,
D. F.
, and
Soh
,
L.-K.
,
2018
, “
Helping Engineering Students Learn in Introductory Computer Science (CS1) Using Computational Creativity Exercises (CCEs)
,”
IEEE Trans. Educ.
,
61
(
3
), pp.
195
203
.10.1109/TE.2018.2804350
23.
Wilson
,
S. E.
,
2023
, “
Exploring Hypothesis-Driven Research Using Arduino Boards and Matlab
,” Activities, Northfield, MN, accessed Feb. 28, 2023, https://serc.carleton.edu/teaching_computation/workshop_2021/activities/arduinoresearch.html
24.
Galina
,
B.
, “
Teaching First-Generation College Students
,”
Vanderbilt University
, Nashville, TN, accessed July 31, 2023, https://cft.vanderbilt.edu/guides-sub-pages/teaching-first-generation-college-students/
25.
Davis
,
J.
,
2012
,
The First Generation Student Experience: Implications for Campus Practice, and Strategies for Improving Persistence and Success
,
Stylus Publishing
, Sterling, VA.
You do not currently have access to this content.