{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T15:57:23Z","timestamp":1777564643282,"version":"3.51.4"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The development of large language models (LLMs) has promoted a transformation of human\u2013computer interaction (HCI) models and has attracted the attention of scholars to the evaluation of personality traits of LLMs. As an important interface for the HCI and human\u2013machine interface (HMI) in the future, the intelligent cockpit has become one of LLM\u2019s most important application scenarios. When in-vehicle intelligent systems based on in-vehicle LLMs begin to become human assistants or even partners, it has become important to study the \u201cpersonality\u201d of in-vehicle LLMs. Referring to the relevant research on personality traits of LLMs, this study selected the psychological scales Big Five Inventory-2 (BFI-2), Myers\u2013Briggs Type Indicator (MBTI), and Short Dark Triad (SD-3) to establish a personality traits evaluation framework for in-vehicle LLMs. Then, we used this framework to evaluate the personality of three in-vehicle LLMs. The results showed that psychological scales can be used to measure the personality traits of in-vehicle LLMs. In-vehicle LLMs showed commonalities in extroversion, agreeableness, conscientiousness, and action patterns, yet differences in openness, perception, decision-making, information acquisition methods, and psychopathy. According to the results, we established anthropomorphic personality personas of different in-vehicle LLMs. This study represents a novel attempt to evaluate the personalities of in-vehicle LLMs. The experimental results deepen our understanding of in-vehicle LLMs and contribute to the further exploration of personalized fine-tuning of in-vehicle LLMs and the improvement in the user experience of the automobile in the future.<\/jats:p>","DOI":"10.3390\/info15110679","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T11:53:43Z","timestamp":1730462023000},"page":"679","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Personality of the Intelligent Cockpit? Exploring the Personality Traits of In-Vehicle LLMs with Psychometrics"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5114-6585","authenticated-orcid":false,"given":"Qianli","family":"Lin","sequence":"first","affiliation":[{"name":"College of Design and Innovation, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhipeng","family":"Hu","sequence":"additional","affiliation":[{"name":"XAI Lab, College of Design and Innovation, Tongji University, Shanghai 200082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Design and Innovation, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"ref_1","unstructured":"OpenAI (2024). GPT-4 Technical Report. arXiv."},{"key":"ref_2","unstructured":"Huang, J., Wang, W., Lam, M.H., Li, E.J., Jiao, W., and Lyu, M.R. (2023). Revisiting the Reliability of Psychological Scales on Large Language Models. arXiv."},{"key":"ref_3","first-page":"10622","article-title":"Evaluating and Inducing Personality in Pre-Trained Language Models","volume":"36","author":"Jiang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Romero, P., Fitz, S., and Nakatsuma, T. (2023). Do GPT Language Models Suffer From Split Personality Disorder? The Advent Of Substrate-Free Psychometrics. arXiv.","DOI":"10.21203\/rs.3.rs-2717108\/v1"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Miotto, M., Rossberg, N., and Kleinberg, B. (2022). Who Is GPT-3? An Exploration of Personality, Values and Demographics. arXiv.","DOI":"10.18653\/v1\/2022.nlpcss-1.24"},{"key":"ref_6","unstructured":"Bodroza, B., Dinic, B.M., and Bojic, L. (2023). Personality Testing of GPT-3: Limited Temporal Reliability, but Highlighted Social Desirability of GPT-3\u2019s Personality Instruments Results. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104145","DOI":"10.1016\/j.artint.2024.104145","article-title":"Exploring the Psychology of LLMs\u2019 Moral and Legal Reasoning","volume":"333","author":"Almeida","year":"2024","journal-title":"Artif. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7115633","DOI":"10.1155\/2024\/7115633","article-title":"The Self-Perception and Political Biases of ChatGPT","volume":"2024","author":"Rutinowski","year":"2024","journal-title":"Hum. Behav. Emerg. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, X., Li, Y., Qiu, L., Joty, S., and Bing, L. (2024). Evaluating Psychological Safety of Large Language Models. arXiv.","DOI":"10.18653\/v1\/2024.emnlp-main.108"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Serapio-Garc\u00eda, G., Safdari, M., Crepy, C., Sun, L., Fitz, S., Romero, P., Abdulhai, M., Faust, A., and Matari\u0107, M. (2023). Personality Traits in Large Language Models. arXiv.","DOI":"10.21203\/rs.3.rs-3296728\/v1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/TIV.2022.3157049","article-title":"Future Directions of Intelligent Vehicles: Potentials, Possibilities, and Perspectives","volume":"7","author":"Cao","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MITS.2022.3202825","article-title":"Parallel Intelligence for Smart Mobility in Cyberphysical Social System-Defined Metaverses: A Report on the International Parallel Driving Alliance","volume":"14","author":"Liu","year":"2022","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/TIV.2023.3339798","article-title":"Intelligent Cockpit for Intelligent Connected Vehicles: Definition, Taxonomy, Technology and Evaluation","volume":"9","author":"Li","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_14","unstructured":"Kurosu, M. (2013). In-Car Information Systems: Matching and Mismatching Personality of Driver with Personality of Car Voice. Human-Computer Interaction. Applications and Services, Springer."},{"key":"ref_15","unstructured":"Alpers, B.S., Cornn, K., Feitzinger, L.E., Khaliq, U., Park, S.Y., Beigi, B., Joseph Hills-Bunnell, D., Hyman, T., Deshpande, K., and Yajima, R. (2020, January 21\u201322). Capturing Passenger Experience in a Ride-Sharing Autonomous Vehicle: The Role of Digital Assistants in User Interface Design. Proceedings of the 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Virtual Event. AutomotiveUI\u201920."},{"key":"ref_16","unstructured":"Kr\u00f6mker, H. (2023). Research on Personality Traits of In-Vehicle Intelligent Voice Assistants to Enhance Driving Experience. HCI in Mobility, Transport, and Automotive Systems, Springer Nature."},{"key":"ref_17","unstructured":"Russell, S.J., and Norvig, P. (2020). What Is AI. Artificial Intelligence: A Modern Approach, Pearson. [4th ed.]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Park, P.S., Schoenegger, P., and Zhu, C. (2023). Diminished Diversity-of-Thought in a Standard Large Language Model. arXiv.","DOI":"10.3758\/s13428-023-02307-x"},{"key":"ref_19","first-page":"51778","article-title":"Evaluating the Moral Beliefs Encoded in LLMs","volume":"36","author":"Scherrer","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","unstructured":"Huang, J., Wang, W., Li, E.J., Lam, M.H., Ren, S., Yuan, Y., Jiao, W., Tu, Z., and Lyu, M. (2024, January 7\u201311). On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLMs. Proceedings of the Twelfth International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.jesp.2014.01.005","article-title":"The Mind in the Machine: Anthropomorphism Increases Trust in an Autonomous Vehicle","volume":"52","author":"Waytz","year":"2014","journal-title":"J. Exp. Soc. Psychol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Esterwood, C., Yang, X.J., and Robert, L.P. (2019). An Automated Vehicle (AV) like Me? The Impact of Personality Similarities and Differences between Humans and AVs. arXiv.","DOI":"10.2139\/ssrn.3446005"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Braun, M., Mainz, A., Chadowitz, R., Pfleging, B., and Alt, F. (2019, January 4\u20139). At Your Service: Designing Voice Assistant Personalities to Improve Automotive User Interfaces. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK.","DOI":"10.1145\/3290605.3300270"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"John, O.P., Donahue, E.M., and Kentle, R.L. (1991). The Big Five Inventory\u2013Versions 4a and 54, Institute of Personality and Social Research, University of California, Berkeley.","DOI":"10.1037\/t07550-000"},{"key":"ref_25","first-page":"114","article-title":"Paradigm Shift to the Integrative Big Five Trait Taxonomy","volume":"3","author":"John","year":"2008","journal-title":"Handb. Personal. Theory Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1037\/pspp0000096","article-title":"The next Big Five Inventory (BFI-2): Developing and Assessing a Hierarchical Model with 15 Facets to Enhance Bandwidth, Fidelity, and Predictive Power","volume":"113","author":"Soto","year":"2017","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_27","unstructured":"Myers, I.B., and McCaulley, M.H. (1985). Manual for the Myers-Briggs Type Indicator, Consulting Psychologists Press."},{"key":"ref_28","unstructured":"Briggs-Myers, I., McCaulley, M.H., Quenk, N.L., and Hammer, A.L. (1998). A Guide to the Development and Use of the Myers-Briggs Type Indicator, Consulting Psychologists Press."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/S0092-6566(02)00505-6","article-title":"The Dark Triad of Personality: Narcissism, Machiavellianism, and Psychopathy","volume":"36","author":"Paulhus","year":"2002","journal-title":"J. Res. Personal."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1177\/1073191113514105","article-title":"Introducing the Short Dark Triad (SD3): A Brief Measure of Dark Personality Traits","volume":"21","author":"Jones","year":"2013","journal-title":"Assessment"},{"key":"ref_31","unstructured":"Degen, H., and Ntoa, S. (2024). A Map of Exploring Human Interaction Patterns with LLM: Insights into Collaboration and Creativity. Artificial Intelligence in HCI, Springer Nature."},{"key":"ref_32","unstructured":"Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., and Lundberg, S. (2023). Sparks of Artificial General Intelligence: Early Experiments with GPT-4. arXiv."},{"key":"ref_33","first-page":"6000","article-title":"Attention Is All You Need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the NAACL-HLT 2019, Minneapolis, MN, USA."},{"key":"ref_35","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020). Language Models Are Few-Shot Learners. arXiv."},{"key":"ref_36","first-page":"66","article-title":"Human-AI Interaction in the Age of Large Language Models","volume":"3","author":"Yang","year":"2024","journal-title":"Proc. AAAI Symp. Ser."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1093\/hcr\/hqac008","article-title":"My AI Friend: How Users of a Social Chatbot Understand Their Human\u2013AI Friendship","volume":"48","author":"Brandtzaeg","year":"2022","journal-title":"Hum. Commun. Res."},{"key":"ref_38","unstructured":"Virvou, M., Tsihrintzis, G.A., and Jain, L.C. (2022). Collaboration in the Machine Age: Trustworthy Human-AI Collaboration. Advances in Selected Artificial Intelligence Areas: World Outstanding Women in Artificial Intelligence, Springer International Publishing."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Karra, S.R., Nguyen, S.T., and Tulabandhula, T. (2023). Estimating the Personality of White-Box Language Models. arXiv.","DOI":"10.2139\/ssrn.4598766"},{"key":"ref_40","unstructured":"SBD Automotive (2023). SBD Explores: The Secret Behind ChatGPT, SBD Automotive. Ref: 2200c-23."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Du, H., Feng, X., Ma, J., Wang, M., Tao, S., Zhong, Y., Li, Y.-F., and Wang, H. (2024). Towards Proactive Interactions for In-Vehicle Conversational Assistants Utilizing Large Language Models. arXiv.","DOI":"10.24963\/ijcai.2024\/869"},{"key":"ref_42","first-page":"53","article-title":"Harnessing Natural Language Processing for Context-Aware, Emotionally Intelligent Human\u2014Vehicle Interaction: Towards Personalized User Experiences in Autonomous Vehicles","volume":"3","author":"Vemoori","year":"2023","journal-title":"J. Artif. Intell. Res. Appl."},{"key":"ref_43","unstructured":"Marcus, A., Rosenzweig, E., and Soares, M.M. (2023). Beyond Car Human-Machine Interface (HMI): Mapping Six Intelligent Modes into Future Cockpit Scenarios. Design, User Experience, and Usability, Springer Nature."},{"key":"ref_44","unstructured":"Karwowski, W., and Ahram, T. (2019). Mixed Reality-Based Platform for Smart Cockpit Design and User Study for Self-Driving Vehicles. Intelligent Human Systems Integration 2019, Springer International Publishing."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1177\/10731911211008245","article-title":"The Big Five Inventory\u20132 in China: A Comprehensive Psychometric Evaluation in Four Diverse Samples","volume":"29","author":"Zhang","year":"2022","journal-title":"Assessment"},{"key":"ref_46","unstructured":"Baynes, H.G. (1923). Psychological Types, Kegan Paul, Trench, Trubner & Co."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1037\/a0025679","article-title":"A Meta-Analysis of the Dark Triad and Work Behavior: A Social Exchange Perspective","volume":"97","author":"Forsyth","year":"2012","journal-title":"J. Appl. Psychol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"S41","DOI":"10.1002\/job.1894","article-title":"The Dark Side of Personality at Work: DARK PERSONALITY REVIEW","volume":"35","author":"Spain","year":"2014","journal-title":"J. Organ. Behav."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/1745691616666070","article-title":"The Malevolent Side of Human Nature: A Meta-Analysis and Critical Review of the Literature on the Dark Triad (Narcissism, Machiavellianism, and Psychopathy)","volume":"12","author":"Muris","year":"2017","journal-title":"Perspect. Psychol. Sci."},{"key":"ref_50","first-page":"102","article-title":"The Big-Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives","volume":"125","author":"John","year":"1999","journal-title":"Psychol. Bull."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"032096","DOI":"10.1088\/1742-6596\/1802\/3\/032096","article-title":"The Design Definition and Research of In-Car Digital AI Assistant","volume":"1802","author":"Ma","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"St\u00f6ckli, L., Joho, L., Lehner, F., and Hanne, T. (2024). The Personification of ChatGPT (GPT-4)\u2014Understanding Its Personality and Adaptability. Information, 15.","DOI":"10.3390\/info15060300"},{"key":"ref_53","unstructured":"Coda-Forno, J., Witte, K., Jagadish, A.K., Binz, M., Akata, Z., and Schulz, E. (2023). Inducing Anxiety in Large Language Models Increases Exploration and Bias. arXiv."},{"key":"ref_54","unstructured":"Huang, J., Lam, M.H., Li, E.J., Ren, S., Wang, W., Jiao, W., Tu, Z., and Lyu, M.R. (2024). Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"18344909231213958","DOI":"10.1177\/18344909231213958","article-title":"Emotional Intelligence of Large Language Models","volume":"17","author":"Wang","year":"2023","journal-title":"J. Pac. Rim Psychol."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/11\/679\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:25:28Z","timestamp":1760113528000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/11\/679"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"references-count":55,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["info15110679"],"URL":"https:\/\/doi.org\/10.3390\/info15110679","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]}}}