ChatGPT-Resistant Screening Instrument for Identifying Non-Programmers

To ensure the validity of software engineering and IT security studies with professional programmers, it is essential to identify participants without programming skills. Existing screening questions are efficient, cheating robust, and effectively differentiate programmers from non-programmers. However, the release of ChatGPT raises concerns about their continued effectiveness in identifying non-programmers. In a simulated attack, we showed that Chat-GPT can easily solve existing screening questions. Therefore, we designed new ChatGPT-resistant screening questions using visual concepts and code comprehension tasks. We evaluated 28 screening questions in an online study with 121 participants involving programmers and non-programmers. Our results showed that questions using visualizations of well-known programming concepts performed best in differentiating between programmers and nonprogrammers. Participants prompted to use ChatGPT struggled to solve the tasks. They considered ChatGPT ineffective and changed their strategy after a few screening questions. In total, we present six ChatGPT-resistant screening questions that effectively identify non-programmers. We provide recommendations on setting up a ChatGPT-resistant screening instrument that takes less than three minutes to complete by excluding 99.47% of non-programmers while including 94.83% of programmers.

• Human-centered computing → Empirical studies in HCI.